The Evolution of Machine Translation Use in the Enterprise

 The modern enterprise with global ambitions is experiencing an evolving view of the value, scope, and need for language translation to enhance and build global business momentum.

The old view has been to translate what is mandatory to participate in a global market, but the new view is increasingly about listening, understanding, sharing, communicating, and engaging the global customer. The new view requires a modern global enterprise to translate millions of words a day.

Recent events, the pandemic, in particular, have forced many private and governmental institutions to become much more focused on expanding their digital presence and profile.

It is now widely understood that providing increasing volumes of content to assist, inform, and help a potential buyer understand the products and services of an enterprise, and enhance the customer’s journey after the purchase are important determinants of sustained market success.

The cost of failure to do so is high. In the last 20 years, over half (52%) of Fortune 500 companies have either gone bankrupt, been acquired, or ceased to exist as a result of the digital and business model disruption. Studies suggest many more companies across various industries will disappear because they fail to understand the strategic value of providing relevant content and establishing a robust digital presence to improve CX.

Innosight Research predicts that as much as 75% of today’s S&P 500 will be replaced by 2027.

Rebecca Ray eloquently describes the impact of producing relevant content for the modern enterprise. She describes how Airbnb and Expedia recognize that they are not just lodging companies, but rather high-tech (multilingual) content companies solving lodging problems.

The [most] significant implications revolve around the recognition given to the business value of multilingual content by a high-tech company such as Airbnb through its financial investment in a cross-functional collaboration initiative to greatly expand language accessibility. They must recognize that, in many cases, their products and services do not function independently of information about them and that the most valuable content and code are often generated by third parties.

As more of the world gets more accustomed to a digital-first buyer journey, companies must adapt to stay relevant. As brands face unmatched logistical and communication challenges in the new millennium, they have focused on more engagement with their customers via digital channels.

Customer experience is no longer just a concept. It’s a business imperative that requires cross-functional collaboration, data, and analytics focused on delivering customer success.

The enterprises with the most successful digital transformation initiatives are seeing growth from improved and innovative digital interactions. This usually means cross-organizational collaboration with a digital strategy-focused team leader invested in optimizing digital channels and improving both customer and employee digital experience. Success is often built on data and analytics and the increasing use of AI to generate predictive analytics to power better, more personalized interactions.

The benefits of providing superior CX are increasingly clear:

• #1 – By 2021, customer experience will overtake price and product as the key brand differentiator – Walker
• 86%– of those who received a great customer experience were likely to repurchase from the same company; compared to just 13% of those who received a poor CX – Temkin Group
• 6x– Between 2010-2015, CX leaders grew 6x faster than CX laggards – Forrester
• “Customer Experience leaders grow revenue faster than CX laggards, drive higher brand preference, and can charge more for their products.” – Forrester’s Rick Parish.
• 3x greater return CX leaders outperform CX laggards in terms of stock performance. -- Watermark Consulting
• Consumers will pay a 16% price premium for a great customer experience. - PwC

Leaders develop “digital agility”, that enables cross-function collaboration focused on mapping and optimizing customer journeys. The key difference according to Altimeter surveys between top-performing companies and average performers is the ability to use data in prescriptive ways. This means harnessing analytics to make or automate decisions that improve processes like delivering a great customer experience, creating a new product, or defining a new strategy.

There is increasing convergence between marketing, sales, and service goals and operations. This convergence is the natural outcome of the increasing digital sophistication of all customer-facing functions.

The Localization Implications

There are significant implications from these larger digital transformation imperatives for traditional localization departments. The needs and scope of language translation for the modern digitally-agile enterprise are significantly greater than ever seen historically.

This is resulting in a changing view of the localization function within the enterprise. Ranging from being seen as a vital partner in global growth strategies to sometimes being relegated to low-value contributors (for those less engaged localization groups) whose only focus is measuring translation quality (badly) and juggling LSP vendors. These latter groups will see customer-facing departments take over large-scale CX-focused translation initiatives and slip into further obscurity.

Airbnb is an example where the localization team is seen as a vital partner in enabling global growth. The Airbnb localization team oversees both typical localization content and user-generated content (UGC), across the organization, which means they oversee billions of words a month being translated across 60+ languages using a combined human plus continuously improving MT translation model. The localization team enables Airbnb to translate customer-related content across the organization at scale. High-value external content is often accorded the same attention as internally produced marketing content.

Digital agility requires that a modern era localization team be the hub of cross-functional collaboration to facilitate and enable all kinds of content to be translated to expedite understanding, communication, and experience of customer-related issues. The modern enterprise will likely need millions of words translated every month to understand, enhance, and improve the global customer experience (GCX). In the B2C scenario, the volumes could even be billions of words per month.

Continuously improving responsive MT is a critical foundation to building better GCX. Increasingly we will see more and more content come directly from carefully optimized MT to the customer without additional human intervention. The linguistic human oversight process and approach will likely change. Upfront human feedback investments will be needed in addition to selective post-editing to make these enterprise MT engines perform optimally on a range of unique and specialized enterprise content. It is possible to post-edit 100K words a month, but it is not possible to do this for a million or a billion source words a month.

We are seeing an emerging translation model (e.g. at Airbnb) that enables an enterprise to build virtuous cycles of continuously improving machine translation working closely together with regular ongoing human feedback.

All focused on cross-functional alignment with the larger enterprise goal of reducing customer friction and increasing customer delight.

Superior CX is built on active, ongoing conversations with the customer to ensure the best possible experience and outcomes. It involves active and continuous listening to the voice of the customer on social platforms to identify problems and success early. It involves multiple levels of communication. Large volumes of multilingual data flows have created a huge and growing need for rapid translation.

Thus, the emerging CX-focused and digitally agile modern enterprise is likely to be one that regularly does the following:

• Helps to provide a uniform customer data profile across the world to achieve a common understanding of the customer.
• Translates over 100 million or even billions of words a month to support GCX in various ways. This means that 95%+ has to be done by optimized adaptive MT engines.
• Has informed human feedback driving continuously learning MT systems. This feedback may only amount to 1% or less of the raw MT that is freely flowing to support understanding, communication, and research of customers across the globe.
• Helps provide a common view on the increasing convergence between marketing, sales, and service content and operations without any language barriers. This has great value for the internal understanding of international customer needs and again enables speedier, better-coordinated responses to differing international customer needs.
• Improves global understanding and communication within and without the enterprise.

The State of Machine Translation in the Enterprise

Machine translation output quality has improved dramatically over the past decade, but all stakeholders should understand that MT is not a perfect replacement for competent human translation. Yet, if properly used and optimized, it can rapidly enhance the transparency of all multilingual data flows in the modern enterprise and expedite international business initiatives.

The use of MT in the enterprise is clearly on the rise. The overall content deluge, the need to monitor brand feedback on social media, and a much more rapid digitally-driven global presence are some of the factors that drive this use.

Global customer service and support and eCommerce have been the most active use cases historically. But increasingly we see that that the global enterprise recognizes the need for a personalized and optimized MT-based translation utility that is secure, private, and enterprise content-tuned to make ALL information multilingual and easier to share, access, and understand.

Recent Changes Driving Faster MT Adoption

• Human evaluations show that neural MT is often indiscernible from human translation for much of the high-value customer service content that is needed for CX improvement.
• Direct customer feedback on the usefulness of MT content suggests that many customers are willing to accept “imperfect” MT if it means that they get broader content access and faster response during many stages of the customer journey.
• The pandemic has made the need for an expanded digital presence much more urgent. MT enables the acceleration of a global “digital-first” strategy.
• The increasing awareness at executive levels of the need for global inclusion and recognition that the fastest-growing markets in the world today are in Africa and SE Asia.
• The increasing awareness in the enterprise of the need and impact of greater availability.
• The increasing importance of community content is increasingly recognized business-enhancing customer creator content.
• An understanding that the next billion potential customers coming to the internet are unlikely to speak English, FIGS, or CJK.

One important point of understanding within a global enterprise is to recognize that different kinds of content need different translation processes. MT is very useful in making high volume, rapidly flowing, short shelf-life content multilingual, but is less suited to high impact executive communications or high liability impact types of content where nuance and semantic accuracy are central.

The mix of human-machine contributions tends to be closely linked to the volume and nuance contained in the source data. The following chart roughly shows the relationship between the content type and the translation approach used. The translation production mode has to be tuned to the needs of target consumers of information and could range from assimilation, dissemination to publishable quality requirements. For example, internal cross-lingual emails can stand a lower average quality translation than customer support content even though both can be voluminous.

What Does an Enterprise Need from an MT Solution?

In considering MT technology options there are several attributes that an enterprise needs to understand in an evaluation. While there is often an over-emphasis and focus on generic, mostly irrelevant MT score-based comparisons, in reality, other factors often matter much more in producing higher ROI and successful outcomes. For example:

Data Security & Privacy: As more CX and confidential product information starts flowing through MT, data security and privacy is our primary concern. CX data may often be considered even more valuable than product information. Many generic public portals present challenges in ensuring the assurance of data privacy. Flexible caching options for different content types are an increasingly important concern.

Rapid Adaptability to Enterprise Content and Use Cases: Enterprise use of MT makes the most sense when the MT performs well on unique enterprise content and terminology. The ability to do this quickly and accurately with minimal startup effort is perhaps the single most valuable aspect of MT technology to an enterprise. There are likely to be many use cases and MT systems like ModernMT, that can leverage existing linguistic assets across multiple use cases with minimal overhead, and system redundancy is much easier to manage and update than a lot of traditional MT systems. Continuous improvement capabilities allow MT systems to evolve and improve over time and thus responsive, highly adaptive MT systems like ModernMT that improve daily are much more likely to produce successful outcomes.

Superior MT Output Quality: While generic MT comparisons by third parties can be useful to understand the potential experience with an MT solution, it is much more important to understand how an MT solution performs on your specific enterprise content. Perhaps even more important is how rapidly and easily an MT system can improve and learn your specific enterprise terminology and linguistic style.

Ease of Integrating Human Corrective Feedback: While some MT systems can produce excellent output occasionally, none can do this all the time.

Human corrective feedback is key to improving MT system performance on an ongoing basis. The ease, speed, and impact with which human feedback is incorporated into driving ongoing MT system performance improvements is a valuable characteristic for an MT system.

ModernMT has been architected to learn rapidly from human corrective feedback and learns continuously and rapidly. The low overhead of adapted models also makes it possible to have hundreds of different models that are easily updated and maintained as improvements can flow from model to model if they have similar content.

Expert Consulting Services: As MT-based translation services become more commonplace in the global enterprise, the reach of rapid translation capabilities will expand and extend through an organization.

It may be necessary to connect different content-containing systems to the MT engine or special linguistic analysis may be needed to speed up the development of systems optimized for new use cases.

MT providers who have this expertise to build a Translation Operating System that can handle a variety of different kinds of data and service types with high efficiency will make the deployment easier, and also increase the probability of success.

ModernMT is a context-aware, incremental, and responsive general-purpose MT technology that is price competitive to the big MT portals (Google, Microsoft, Amazon) and is uniquely optimized for LSPs and global enterprises, and addresses all the criteria specified above.

ModernMT can be kept completely secure and private for those willing to make the hardware investments for an on-premise installation. It is also possible to develop a secure and private cloud instance for those who wish to avoid making hardware investments.

ModernMT overcomes technology barriers that hinder the wider adoption of currently available MT software by enterprise users and language service providers:

• ModernMT is a ready-to-run application that does not require any initial training phase. It incorporates user-supplied resources immediately without needing laborious, technically overly complex, and tedious upfront model training.
• ModernMT learns continuously and instantly from user feedback and corrections made to MT output as production work is being done. It produces output that improves by the day and even the hour in active-use scenarios.
• ModernMT manages context automatically and does not require building multiple different domain-specific and use-case-specific systems.
• ModernMT has a data collection infrastructure that accelerates the process of filling the data gap between baseline systems and enterprise-specific models with unique terminology and linguistic style.
• ModernMT is responsive to small volumes of corrective feedback and thus allows straightforward deployment in multiple organizational scenarios.
• ModernMT’s goal is to deliver the quality of multiple custom engines by adapting to the provided context on the fly. This fluidity makes it much easier to manage on an ongoing basis.
• ModernMT provides extensive control over the MT cache allowing immediate no-trace deletion to a permanent (annual) cache for highly static public content.
• ModernMT can scale from millions to billions of words a day with responsive and modifiable performance through the range.
• ModernMT systems can easily outperform competitive systems once adaptation begins, and active corrective feedback immediately generates quality-improving momentum.

Thus as we head into the post-pandemic era, the need for a broadly capable, private, secure, corporate-domain tuned  "translation engine" will only grow in importance for the digitally agile, CX-savvy global enterprise.

Localization teams will need to lead multi-year investment initiatives that are recognized by executives as essential drivers for their organizations’ global growth and revenue to enable this.  

The shift to language as a feature at the platform level wherein language is designed, delivered, and optimized as a feature of a product and/or service from the beginning is now underway.

As Airbnb CEO, Brian Chesky stated in his recent launch video, “Technology made it possible to work from home, but Airbnb now allows you to work from any home.” Obviously, language independence is an integral part of his platform that now makes this possible. His success will demand that others follow.

This post was originally published here. 

NiuTrans: An Emerging Enterprise MT Provider from China

 This post highlights a Chinese MT vendor who I suspect is not well known in the US or Europe currently, but who I expect will become better known over the coming years. While the US giants (FAAMG) still dominate the MT landscape around the world today, I think it is increasingly possible that other players from around the world, especially from China may become much more recognized in the future. 

One indicator that has been historically reliable to forecast and predict emerging economic power is the volume of patent filings in a country. This has been true for Japan and Germany historically where we saw voluminous patent activity precede the economic rise of these countries, and recently we see that this predictor is also aligned with the rise of S. Korea and China as economic powerhouses. However, the sheer volume of filings is not necessarily a lead indicator of true innovation, and some experts say that the volume of patents filed and granted abroad is a better indicator of innovation and patent quality. But today we see emerging giants from Asia in consumer electronics, automobiles, eCommerce, internet services, and nobody questions the building innovation momentum happening in Asia today. 

Artificial Intelligence (AI) is heralded by many as a key driver of wealth creation for the next 50 years. To build momentum with AI requires a combination of access to large volumes of "good" data, computing resources, and deep expertise in machine learning, NLP, and other closely related technologies. Today, the US and China look poised to be the dominant players in the wider application of AI and machine learning-based technologies with a few others close behind. And here too deep knowledge and clout are indicated by the volume of influential papers published and referenced by the global community. A recent analysis, by the Allen Institute for Artificial Intelligence in Seattle, Washington found that China has steadily increased its share of authorship of the top 10% most-cited papers. The researchers found that America’s share of the most-cited 10 percent of papers declined from a high of 47 percent in 1982 to a low of 29 percent in 2018. China’s share, meanwhile, has been “rising steeply,” reaching a high of 26.5 percent last year, Though the US still has significant advantages with the relative supply of expert manpower and dominance in manufacture of AI semiconductor chip technology, this too is slowly changing even though most experts expect the US to maintain leadership for other reasons. 

Credit: Allen Institute for Artificial Intelligence

These trends also impact the translation industry and they change the relative benefit and economic value of different languages. The global market is slowly changing from a FIGS-centric view of the world to one where both the most important source language (ZH, KO, HI) and target languages are changing.  The fastest-growing economies today are in Africa and Asia and are not likely to be well served by a FIGS-centric view though it appears that English will remain a critical world language for knowledge sharing for at least another 25 years. These changes create an opportunity for agile and skillful Asian technology entrepreneurs like NiuTrans who are much more tuned-in to this rapidly evolving world.  I have noted that some of the most capable new MT initiatives I have seen in the last few years were based in China. India has lagged far behind with MT, even though the need there is much greater, because of the myth that English matters more, and possibly because of the lack of governmental support and sponsorship of NLP research.

The Chinese MT Market: A Quick Overview

I recently sat down with Chungliang Zhang from NiuTrans, an emerging enterprise MT vendor in China, to discuss the Chinese MT market and his company’s own MT offerings. He pointed out that China is the second-largest global economy today, and it is now increasingly commonplace for both Chinese individuals and enterprises to have active global interactions. The economic momentum naturally drives the demand for automated translation services.

Some examples, he pointed out:

In 2019, China’s outbound tourist traffic totaled 155M people, up 3.3% from the previous year. This massive volume of traveler traffic results in a concomitant demand for language translation. Chungliang pointed out that this travel momentum significantly drives the need for voice translation devices in the consumer market like those produced by Sougou, iFlyTek, and others, which have been very much in demand in the last few years.

There is also a growing interest by Chinese enterprises, both state-owned or privately owned, to build and expand their business presence in global markets. For example, Alibaba, China’s largest eCommerce company, is listed on the NYSE and has established an international B2B portal (Alibaba.com) where 20 million enterprises gather and work to “Buy Global, Sell Global.” Currently, the Alibaba MT team builds the largest eCommerce MT systems globally, often reaching volumes of 1.79 billion translation calls per day, which is a larger transaction volume than either Google or Amazon.

“All in all, as we can see it, there is a clear trend that MT is increasingly being used in more and more industries, such as language service industries, intellectual property services, pharmaceutical industries, and information analysis services.”

While it is clear that consumers and individuals worldwide are regularly using MT, the primary enterprise users of MT in China are government agencies and internet-based businesses like eCommerce. This need for translation is now expanding to more enterprises who seek to increase their international business presence and realize that MT can enable and accelerate these initiatives.

The Chinese MT technology leaders in terms of volume and regular user base are the internet services giants (such as Baidu, Tencent, Alibaba, Sogou, Netease) or the AI tech giants (such as iFlyTek). Google Translate and Microsoft Bing Translator are also popular in China since they are free, but they don’t have a large share of the total use if the focus is strictly on MT technology.

When asked to comment on the characteristics and changes in the Chinese MT market, Chungliang said:

“In our understanding, Sogou and iFlytek's primary business focus is the B2C market, and thus both of them develop consumer hardware like personal voice translators. Sogou was recently (July 29, 2020) purchased by Tencent (a major social media player), so we don’t know what will happen next. iFlytek is famous for its Speech-To-Speech technology capabilities. Thus it is natural for them to develop MT, to get the two technologies integrated and grab a larger share of the market.

As for the other important MT players in China, Alibaba MT mainly serves its own global focused eCommerce business, and Tencent Translate focuses on providing the translation needs of its users in social networking use scenarios. Like Google Translate, Baidu Translate is a portal to attract individual users who might need translation during a search. It also serves to expand Baidu’s influence as a whole. While Netease Youdao focuses on the education industry, and the Youdao Team integrates the Youdao online dictionary, direct MT, and human translation.

What are the main languages that people/customers translate? As far as we know, the most translated language is English, Japanese is second, followed by Arabic, Korean, Thai, Russian, German, and Spanish.” Of course, this is all direct to and from Chinese.”

NiuTrans Focus: The Enterprise

The NiuTrans team learned very early in their operational history and during their startup phase that their business survival was linked to providing MT services for the enterprise rather than for individual users and consumers. The market for individuals is dominated by offerings like Google Translate and Baidu Translate that offer virtually-free services. In contrast, NiuTrans is focused on meeting the enterprise demands for MT, which often means deploying on-premise MT engines and the development of custom engines. These enterprises tend to be concentrated around Intellectual Property and Patent services, Pharmaceuticals, Vehicle Manufacturing, IT, Education, and AI companies. For example, NiuTrans builds customized patent-domain MT engines for the China Patent Information Center (CNPAT, a branch of the China National Intellectual Property Administration, a large-scale patent information service based in Beijing.)

CNPAT has the largest collections of multilingual parallel data for patents, and services ongoing and substantial demands for patent-related MT needs in various use scenarios such as patent application filing and examination, patent-related transactions, and patent-based lawsuits. Given the scale of the client’s needs, NiuTrans sends an R&D team on-site to work with CNPAT’s technical team for data processing and data cleaning. This data is then used in the NiuTrans.NMT training module to develop patent-domain NMT engines on CNPAT’s on-premise servers. The on-site team also develops custom MT APIs on-demand to fit into CNPAT’s current workflow and customer servicing needs.

Besides powering and enabling the specialized translation needs of services like CNPAT, NiuTrans also provides back-end MT services for industrial leaders, including iFlyTek (also an early investor in NiuTrans), JD.com (the No. 2 eCommerce business in China), Tencent (the largest social networking company in China), Xiaomi (a leader of smart devices OEMs in China), and Kingsoft (a leader of office software in China).

NiuTrans has an online cloud API that also attracts 100,000+ small and medium enterprises interested in expanding their international operations and business presence. The pricing for these smaller users are based on the volume of characters these users translate and is much lower than Google Translate and Baidu Translate prices.

NiuTran’ Online Cloud User Locations

You can visit the NiuTrans Translate portal at https://niutrans.com

NiuTrans write and maintain their own NMT code-base rather than use open source options for NiuTrans.NMT and claim that they achieve comparable, if not better, quality performance with their competitors. Their comparative performance at the WMT19 evaluations suggests that they actually do better than most of their competitors. They are not dependent on TensorFlow, PyTorch, or OpenNMT to build their systems. Today, NiuTrans is a key MT technology provider, especially for enterprises in China.

NiuTrans.NMT is a lightweight and efficient Transformer-based neural machine translation system. Its main features are:

• Few dependencies. It is implemented with pure C++, and all dependencies are optional.
• Fast decoding. It supports various decoding acceleration strategies, such as batch pruning and dynamic batch size.
• Advanced NMT models, such as Deep Transformer.
• Flexible running modes. The system can be run on various systems and devices (Linux vs. Windows, CPUs vs. GPUs, FP32 vs. FP16, etc.).
• Framework agnostic. It supports various models trained with other tools, e.g., Fairseq models.
• The code is simple and friendly to beginners.

When I probed into why NiuTrans had chosen to develop their own NMT technology rather than use the widely accepted open-source solutions, I was provided with a history of the company and its evolution through various approaches to developing MT technology.

The NiuTrans team originated in the NLP Lab at Northeastern University, China (NEUNLP Lab), a machine translation research leader in the Chinese academic world going as far back as 1980. Like many elsewhere in the world, the team initially studied rule-based MT from 1980 to 2005. In 2006 Professor Jingbo Zhu (the current Chairman of NiuTrans) returned from a year-long visit to ISI-USC and decided to switch to statistical MT research working together with Tong Xiao, who was a fresh graduate student at the time and is now the CEO of NiuTrans. They made rapid strides in SMT research, releasing the first version of NiuTrans SMT open source in 2011. At that time, Chinese academia primarily used Moses to conduct MT-related research and develop MT engines. The development of the NiuTrans.SMT open-source proved that Chinese engineers could do the same as, or even better than Moses, and also helped to showcase the strength and competence of the NiuTrans team. Thus, in 2012, confident with their MT technology and armed with a dream to expand the potential of this technology to connect the world with MT, the NiuTrans team decided to form an MT company, converting the 30+ years’ of MT research work to developing MT software for industrial use.

Given their origins in academia, they kept a close watch on MT research and breakthroughs worldwide and noticed in 2014 that there was a growing base of research being done with neural network-based deep learning models. Therefore, the NiuTrans team started studying deep learning technologies in 2015 and released its first version of NiuTrans.NMT in December 2016, just three months after Google announced the release of its first NMT engines.

NiuTrans prefers to avoid using open source MT platforms like TensorFlow, PyTorch, or OpenNMT as they have developed deep competence in MT technology gathered over 40 years of engagement. The leadership believes there are specific advantages to building the whole technology stack for MT and intend to continue with this basic development strategy. As an example, Chunliang pointed me to the release of NiuTensor, their own deep learning tool: (https://github.com/NiuTrans/NiuTensor) and NiuTrans.NMT Open Source (https://github.com/NiuTrans/NiuTrans.NMT). They are confident that they can keep pace with continuous improvements in open source with support from the NEUNLP Lab, which has eight permanent staff and 40+ Ph.D./MS students focusing on MT issues of relevance and interest for their overall mission. This group also allows NiuTrans to stay abreast of the worldwide research being done elsewhere.

NiuTrans understands that a critical requirement for an enterprise user is to adapt and customize the MT system to enterprise-specific terminology or use. Thus, it provides both a user terminology module to introduce user terminology into the MT system and a user translation memory module to introduce the users’ sentence pairs to tune the MT system. Another more sophisticated solution is incremental training. They incorporate user data to modify the NiuTrans model parameters to get the MT model better adjusted to user data features.

NiuTrans also gathers post-editing feedback on critical language pairs like ZH <> EN and ZH <> JP on an ongoing basis, then analyze error patterns to develop continuing engine performance improvements.

Quality Improvement, Data Security, and Deployment

NiuTrans evaluates MT system performance using BLEU and a human evaluation technique that ranks relative systems. They prefer not to use the widely used 5-point scale to assign an absolute value to a translation. Thus if they were comparing NiuTrans, Google, and DeepL, they would use a combination of BLEU and have humans rank the same blind test set for the three systems.

NiuTrans also has an ongoing program to improve its MT engines continually. They do this in three different ways:

1. Firstly, as the company has a strong research team that is continually experimenting and evaluating new research, the impact of this research is continuously tested to determine if it can be incorporated into the existing model framework. This kind of significant technical innovation is added into the model two or three times a year.
2. Secondly, customer feedback, ongoing error analysis, or specialized human evaluation feedback also trigger regular updates to the most important MT systems (e.g. ZH<>EN) at least once a month.
3. Thirdly, engines will be updated as new data is discovered, gathered, or provided by new clients. High-quality training data is always sought after and considered valuable to drive ongoing MT system improvements.

NiuTrans has performed well in comparative evaluations of their MT systems against other academic and large online MT solutions. Here is a summary of the results from WMT19. They report that their performance in WMT20 is also excellent, but final results have not yet been published.

NiuTrans training data comes mainly from two sources: data crawling and data purchase from reliable vendors.

NiuTrans uses crawlers to collect the parallel texts from the websites that do not prohibit or prevent this, e.g., some Chinese government agencies’ websites that often provide data in several languages. They also buy parallel sentences (TM) and dictionaries from specific data provider companies, who might require signing an agreement, specifying that the data provider retains the intellectual property rights of the data.

NiuTrans gets the bulk of its revenue from data-security concerned customers who deploy their MT systems on On-premise systems. However, NiuTrans is also working on an Open Cloud https://niutrans.com offering, allowing customers to access an online API and avoid installing the infrastructure needed to set up on-premise systems. The Open Cloud is a more cost-effective option for smaller SME companies, and NiuTrans has seen rapid adoption of this new deployment in specific market segments.

International customers, especially the larger ones, much prefer to deploy their NiuTrans MT systems on-premise. For those international customers who cannot afford on-premise systems, the NiuTrans Open Cloud solution is an option. This system is deployed on the Alibaba Cloud that is governed by Chinese internet security laws that require that user data be kept for six months before deletion. The company plans to build another cloud service on the Amazon Cloud for international customers who have data security concerns. This new capability will allow users to encrypt their data locally, transfer the data securely to the Amazon Cloud. NiuTrans will then decrypt the source data on their servers, translate it, and finally delete all the user data and the corresponding translation results once the source data has been translated.

NiuTrans currently has 100+ employees, directed by Dr. Jjingbo Zhu and Dr. Tong Xiao, two leading MT scientists in China. Shenyang is the seat of the company’s headquarters and R&D team as well. Technical support and services are available in Beijing, Shanghai, Hangzhou, Chendu, and Shenzhen currently, but the company is now exploring entering the Japanese market, with the assistance of partners in Tokyo and Osaka. While NiuTrans is not a well-known name in the US/EU translation industry today, I suspect that they will become an increasingly better-known provider of enterprise MT technology in the future.

Computers that "understand" language: BERT Explained

As the momentum with machine learning research continues, we continue to hear about ongoing research breakthroughs, and perhaps the most significant natural language processing (NLP) advance in the last 12 months was something called BERT. The translation industry deals with words, and thus any technology advance that improves capabilities in transforming words from one language to another is considered to be most important and impactful. Therefore, there is a tendency to place new inventions always in the translation context within the industry. This tendency does not always make sense, and this post will attempt to clarify and explain BERT in enough detail to help readers better understand the relevance and use of this innovation within the confines of the translation industry.

What is BERT?

Last year, Google released a neural network-based technique for natural language processing (NLP) pre-training called Bidirectional Encoder Representations from Transformers (BERT). While that sounds like quite a mouthful, BERT is a significant, and possibly even revolutionary step forward for NLP in general. There has been much excitement in the NLP research community about BERT because it enables substantial improvements in a broad range of different NLP tasks. Simply put, BERT brings considerable advances to many tasks related to natural language understanding (NLU).

The following points help us to understand the salient characteristics of the innovation, driven by BERT :

• BERT is pre-trained on a large corpus of annotated data that enhances and improves subsequent NLP tasks. This pre-training step is half the magic behind BERT's success. It is because as we train a model on a large text corpus, the model starts to pick up the more in-depth and intimate understandings of how the language works. This knowledge is the swiss army knife that is useful for almost any NLP task. For example, a BERT model can be fine-tuned toward a small data NLP task like question answering and sentiment analysis, resulting in substantial accuracy improvements compared to training on smaller datasets from scratch. BERT allows researchers to get state-of-the-art results even when very little training data is available
• Words are problematic because plenty of them are ambiguous, polysemous, and synonymous. BERT is designed to help solve ambiguous sentences and phrases that are made up of lots and lots of words with multiple meanings.
• BERT will help with things like:

• Named entity determination.
• Coreference resolution.
• Question answering.
• Word sense disambiguation.
• Automatic summarization.
• Polysemy resolution

• BERT is a single model and architecture that brings improvements in many different tasks that previously would have required the use of multiple different models and architectures.
• BERT also provides a much better contextual sense, and thus increases the probability of understanding the intent in search. Google called this update "the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search."

While BERT is a significant improvement in how computers "understand" human language, it is still far away from understanding language and context in the same way that humans do. We should, however, expect that BERT will have a significant impact on many understanding focused NLP initiatives. The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another. The datasets are designed to test a model's language understanding and are useful for evaluating models like BERT. As the GLUE results show, BERT makes it possible to outperform humans even in comprehension tasks previously thought to be impossible for computers to outperform humans.

To better understand this, I recently sat down with SDL NLP experts, Dragos Munteanu and Steve DeNeefe. I asked them questions to help us all better understand BERT and its possible impact on other areas of language technology.

1. Can you describe in layman's terms what BERT is and why there is so much excitement about it?

BERT combines three factors in a powerful way. First, it is a very large, attention-based neural network architecture known as "Transformer Encoder" (Transformer networks are the basis of our NMT 2.0 language pairs). Second, one uses a "fill in the blank" method to train the network, where you remove words randomly from a paragraph then the system tries to predict them. Third, it is trained on massive amounts of monolingual text, usually English. There are also variants trained with French, German, Chinese, and even one trained with 104 different languages (but not with parallel data).

The result is a powerful representation of language in context, which can be "fine-tuned" (quickly adapted) to perform many challenging tasks previously considered hard for computers, i.e., requiring world knowledge or common sense.

2. Some feel that BERT is good for anything related to NLP, but what are the specific NLP problems that BERT has solved best?

BERT and other similar models (RoBERTa, OpenAI GPT, XL-Net) are state of the art on many NLP tasks that require classification, sequence labeling, or similar digesting of text, e.g., named entity recognition, question answering, sentiment analysis. BERT is an encoder, so it digests data, but by itself, it does not produce data. Many NLP tasks also include a data creation task (e.g., abstractive summarization, translation), and these require an additional network and more training.

3. I am aware that several BERT inspired initiatives are beating human baselines on the GLUE (General Language Understanding Evaluation) leaderboard. Does this mean that computers understand our language?

Neural networks learn a mapping from inputs to outputs by finding patterns in their training data. Deep networks have millions of parameters and thus can learn (or "fit") quite intricate patterns. This is really what enables BERT to perform so well on these tasks. In my opinion, this is not equivalent to an understanding of the language. There are several papers and opinion pieces that analyze these models' behavior on a deeper level, specifically trying to gauge how they handle more complex linguistic situations, and they also conclude that we are still far from real understanding. Speaking from the SDL experience training and evaluating BERT based models for tasks such as sentiment analysis or question answering, we recognize these models perform impressively. However, they still make many mistakes that most people would never make.

4. If summarization is a key strength of BERT – do you see it making its way into the Content Assistant capabilities that SDL has as the CA scales up to solve larger enterprise problems related to language understanding?

Summarization is one of the critical capabilities in CA, and one of the most helpful in enabling content understanding, which is our overarching goal. We currently do extractive summarization, where we select the most relevant segments from the document. In recognition of the fact that different people care about various aspects of the content, we implemented an adaptive extractive summarization capability. Our users can select critical phrases of particular interest to them, and the summary will change to choose segments that are more related to those phrases. 

Another approach is abstractive summarization, where the algorithm generates new language that did not exist in the document being summarized. Given its powerful representation, transformer networks like BERT are in a good position to generate text that is both fluent and relevant for the document's meaning. In our experiments so far, however, we have not seen compelling evidence that abstractive summaries facilitate better content understanding.

5. I have read that while there are some benefits to trying to combine BERT-based language models into NMT systems; however, the process to do this seemed rather complicated and expensive in resource terms. Do you see BERT influenced capabilities coming to NMT in the near or distant future?

Pretrained BERT models can be used in several different ways to improve the performance of NMT systems, although there are various technical difficulties. BERT models are built on the same transformer architecture used in the current state-of-the-art NMT systems. Thus, in principle, a BERT model can be used either to replace the encoder part of the NMT system or to help initialize the NMT model's parameters. One of the problems is that, as you mention, using a BERT model increases the computational complexity. And the gains for MT are, so far, not that impressive; nowhere near the kind of benefits that BERT brings on the GLUE-style tasks. However, we continue to look into various ways of exploiting the linguistic knowledge encoded in BERT models to make our MT systems better and more robust.

6. Others say that BERT could help in developing NMT systems for low-resource languages. Are the capabilities to transfer the learning from monolingual data likely to affect our ability to see more low-resource language combinations shortly?

One of the aspects of BERT that is most relevant here is the idea of training representations that can be quickly fine-tuned for different tasks. If you combine this with the concept of multi-lingual models, you can imagine an architecture/training procedure that learns and builds models relevant to several languages (maybe from the same family), which then can be fine-tuned for any language in the family with small amounts of parallel data.

7. What are some of the specific capabilities, resources, and competence that SDL has that will enable the company to adopt this kind of breakthrough technology faster?

 Our group has expertise in all the areas involved in developing, optimizing, and deploying NLP technologies for commercial use cases. We have world-class researchers who are recognized in their respective fields, and regularly publish papers in peer-reviewed conferences. 

We have already built a variety of NLP capabilities using state-of-the-art technology: summarization, named entity recognition, question generation, question answering, sentiment analysis, sentence auto-completion. They are in various stages in the research-to-production spectrum, and we continue to develop new ones. 

We also, have expertise in the deployment of large and complex deep learning models. Our technology is designed to optimally use either CPUs or GPUs, in either 32-bit or 16-bit mode, and we understand how to make quality-speed trade-offs to fulfill the various use cases of our customers. 

Last but not least, we foster close collaboration between research, engineering, and product management. Developing NLP capabilities that bring concrete commercial value is as much an art as it is a science, and success can only be attained through the breadth of expertise and deep collaboration.

In a Funk about BLEU

MT quality measurement, like human translation quality measurement, has been a difficult and challenging subject for both the translation industry and for many MT researchers and systems developers as the most commonly used metric BLEU, is now quite widely understood to be of especially limited value with NMT systems. 

Most of the other text-matching NLP scoring measures are just as suspect, and practitioners are reluctant to adopt them as they are either difficult to implement, or the interpretation pitfalls and nuances of these other measures are not well understood. They all can generate a numeric score based on various calculations of Precision and Recall that need to be interpreted with great care. Most experts will say that the only reliable measures are those done by competent humans and increasingly best practices suggest that a trust-but-verify approach is better. There are many variations of superficially accurate measures available today, but on closer examination, they are all lacking critical elements to make them entirely reliable and foolproof.

So, as much as BLEU scores suck, we continue to use them since some, or perhaps even many of us understand them. Unfortunately, many still don't have a real clue, especially in the translation industry. 

I wonder sometimes if all this angst about MT quality measurement is much ado about nothing. We do in fact, need very rough indicators of MT quality to make judgments of suitability in business use cases, but taking these scores as final indicators of true quality is problematic. It is likely that the top 5, or even top 10 systems are essentially equivalent in terms of the MT quality impact on the business purpose. The real difference in business impact comes from other drivers: competence, experience, process efficiency and quality of implementation.

I would argue that even for localization use cases, the overall process design and other factors matter more than the MT output quality.

 As we have said before, technology has value when it produces favorable business outcomes, even if these outcomes can be somewhat challenging to measure with a precise and meaningful grade. MT is a technology that is seldom perfect, but even in its imperfection can provide great value to an enterprise with a global presence. MT systems with better BLEU or Lepor scores do not necessarily produce better business outcomes. I would argue that an enterprise could use pretty much any "serious" MT system without any impact on the final business outcome. 

This is most clear with eCommerce and global customer service and support use cases, where the use of MT can very rapidly yield a significant ROI. 

"eBay’s push for machine translation has helped the company increase Latin American exports by nearly 20%, according to researchers from the Massachusetts Institute of Technology, and illustrates the potential for increased commercial activity as translation technologies gain wider adoption in business."

MT deployment use case presentations shared by practitioners who have used MT to translate large volumes of knowledgebase support content show that what matters is whether the content helps customers across the globe get to answers that solve problems faster. Translation quality matters but only if it helps understandability. In the digital world, speed is crucial and often more important.

Some 100,000 buyers exchange a total of 2 billion translated text messages every week on the Alibaba.com global-trade platform. This velocity and volume of communication that is enabled by MT enable new levels of global commerce and trade. How many of these messages do you think are perfect translations? 

A monolingual Live Support Agent who can service thousands of global customers a week because he/she can quickly understand the question and send back relevant and useful support content back to a customer using MT  is another example. The ability to do this in volume matters more than perfect linguistic quality.

So then the selection of the right MT technology or solution will come down to much more enterprise relevant issues like:

• Data Security & Privacy 
• Adaptability to enterprise unique terminology and use cases
• Scalability - from billions of words to thousands per hour 
• Deployment Flexibility - On-premise, cloud or combinations of both
• Integration with key IT infrastructure and platforms
• Availability of expert consulting services for specialization 
• Vendor focus on SOTA
• MT system manageability
• Cost 
• Vendor reputation, profile and enterprise account management capabilities

Pete Smith will be presenting more details of his research study at SDL Connect next month.

===============

There is little debate: the machine translation research and practitioner communities are in a funk about BLEU. From recent webinars to professional interviews and scholarly publications, BLEU is being called on the carpet for its technical shortcomings in the face of a rapidly-developing field, as well as the lack of insight it provides to different consumers such as purchasers of MT services or systems.

BLEU itself is used widely, especially in the MT research community, as an outcome measure for evaluating MT. Yet even in that setting, there is considerable rethinking and re-evaluation of the metric, and BLEU has been an active topic of critical discussion and research for some years, including the challenges faced by evaluating automated translation across the language typology spectrum and especially in cases of morphologically rich languages. And the issue is not limited, of course, to machine translation—the metric is also a topic in NLP and natural language generation discussions generally.

BLEU’s strengths and shortcomings are well-known. At its core, BLEU is a string matching algorithm for use in evaluating MT output and is not per se a measure of translation quality. That said, here is no doubt that automated or calculated metrics are of great value, as total global MT output approaches levels of one trillion words per day.

And few would argue that, in producing and evaluating MT or translation in general, context matters. A general-purpose, public-facing MT engine designed for broad coverage among users and use cases is just that—general-purpose, and likely more challenged by perennial source language challenges such as specific domain style/terminology, informal language usage, regional language variations, and other issues.

It is no secret that many MT products are trained (at least initially) on publicly available research data and that there are, overall, real thematic biases in those datasets. News, current events, governmental and parliamentary data sets are available across a wide array of language pairs, as well as smaller amounts of data from domains such as legal, entertainment, and lecture source materials such as TED Talks. Increasingly, datasets are available in the IT and technical domains, but there are few public bilingual datasets available that are suitable for major business applications of MT technology such as e-commerce, communication, and collaboration, or customer service.

Researchers and applied practitioners have all benefited from these publicly-available resources. But the case for clarity is perhaps most evident in the MT practitioner community.

For example, enterprise customers hoping to purchase machine translation services face a dilemma: how might the enterprise evaluate an MT product or service for their particular domain, and with more nuance and depth than simply relying on marketing materials boasting scores or gains in BLEU or LEPOR? How might you evaluate major vendors of MT services specific to your use case and needs?

And as a complicating factor, we know an increasing amount about the “whys” and “hows” of fine-tuning general-purpose engines to better perform in enterprise cases such as e-commerce product listings, technical support knowledgebase content, social media analysis, and user feedback/reviews. In particular, raw “utterances” from customers and customer support personnel in these settings are authentic language, with all of its “messiness.”

The UTA research group has recently been exploring MT engine performance on customer support content, building a specialized test set compiled from source corpora including email and customer communications, communications via social media, and online customer support. In particular, we explored the utilization of automation and standard NLP-style pre-processing to rapidly construct a representative translation test set for the focused use case.

At the start, an initial set of approximately 3 million English sentence strings related to enterprise communication and collaboration were selected. Source corpora represented tasks such as email communication, customer communications, communications via social media, and online customer support.

Candidate sentence strings from these larger corpora were narrowed via a sentence clustering technique, training a FastText model on the input documents to capture both the semantic and non-semantic (linguistic) properties of the corpora. To give some sense of the linguistic features considered in string selection, corpora elements were parsed using the spaCy natural language processing library’s largest English model to consider features in a string such as the number of “stop words”; the number of tokens that were punctuation, numbers, e-mail addresses, URLs, alpha-only, and out-of-vocabulary; the number of unique lemmas and orthographic forms; number of named entities; the number of times each entity type, part-of-speech tag and dependency relation appeared in the text; and the total number of tokens. Dimensionality reduction and clustering were used in the end, to result in 1050 English-language strings for the basic bespoke test set.

The strings from the constructed set were translated into seven languages (French, German, Hindi, Korean, Portuguese, Russian, Spanish) by professional translators. Then the translated sentences from the test set were utilized as translation prompts in seven language pairs (English-French, English-German, English-Hindi, English-Korean, English-Portuguese, English-Russian, English-Spanish) by four major, publicly-available MT engines via API or web interface. At both the corpus as well as the individual string level, BLEU, METEOR, and TER scores were generated for each major engine and language pair (not all of the seven languages were represented in all engine products).

Our overall question was: does BLEU (or any of the other automated scores) support, say, the choice of engine A over engine B for enterprise purchase when the use case is centered on customer-facing and customer-generated communications? 

To be sure, the output scores presented a muddled picture. Composite scores of the general-purpose engines clustered within approximately 5-8 BLEU points of each other in most languages. And although we used a domain-specific test set, little in the results would have provided the enterprise-level customer with a clear path forward. As Kirti Vashee has pointed out recently, in responding effectively to the realities of the digital world, “5 BLEU points this way or that is negligible in most high-value business use cases.”

What are some of the challenges of authentic, customer language? Two known challenges to MT include the formality/informality of language utterances and emotive content. The double-punch of informality and emotion-laden customer utterances pose a particularly challenging case.

As we reviewed in a recent webinar, customer-generated strings in support conversations or online interactions present a translator with a variety of expressions of emotion, tone, humor, sarcasm, all embedded within a more informal and Internet-influenced style of language. Some examples included:

             Support…I f***ing hate you all. [Not redacted in the original.]
            Those late in the day deliveries go “missing” a lot.
            Nope didn’t turn up…just as expected…now what dude?
            I feel you man, have a good rest of your day!
           Seriously, this is not OK.
           A bunch of robots who repeat the same thing over & over.
           #howdoyoustayinbusiness

Here one can quickly see how an engine trained primarily with formal, governmental or newspaper source would be quickly challenged. But in early results, our attempts to unpack the issues of how MT may perform on emotive content (i.e., not news, legal, or technical content) have provided little insight to date. Early findings suggest surprisingly little interaction between standard ratings of sentiment and emotion run on the test set individual strings (VADER positive, negative, neutral, composite and IBM tone analysis) and variance in downstream BLEU scores.

Interestingly, as an aside in our early work, raw BLEU scores across languages for the entire test set did generally correlate comparatively highly with METEOR scores. Although this correlation is expected, the strength of the relationship was surprising in an NMT context, as high as r=.9 across 1000+ strings in a given language pair. If, as the argument goes, NMT brings strengths in fluency which includes elements METEOR scoring is, by design, more sensitive to (such as synonyms or paraphrasing), one might expect that correlation to be weaker. More broadly, these and other questions around automatic evaluation have a long history of consideration by the MT and WMT communities.

One clearly emerging practice in the field is to combine an automated metric such as BLEU along with human evaluation on a smaller data set, to confirm and assure that the automated metrics are useful and provide critical insight, especially if the evaluation is used to compare MT systems. Kirti Vashee, Alon Lavie, and Daniel Marcu have all written on this topic recently.

Thus, the developing, more nuanced understanding of the value of BLEU may be as automated scores seen as initially most useful during MT research and system development, where they are by far the most widely-cited standard. The recent Machine Translation Summit XVII in Dublin, for example, had almost 500 mentions or references to BLEU in the research proceedings alone.

But this measure may be potentially less accurate or insightful when broadly comparing different MT systems within the practitioner world, and perhaps more insightful again to both researcher and practitioner when paired with human or other ratings. As one early MT researcher has noted, “BLEU is easy to criticize, but hard to get away from!”

Discussions at the recent TAUS Global Content Conference 2019 further developed the ideas of MT engine specialization in the context of the modern enterprise content workflow. Presenters such as SDL and others offered views future visions of content development personalization and use in a multilingual world. These future workflows may contain hundreds or thousands of specialized, specially-trained and uniquely maintained automated translation engines and other linguistic algorithms, as content is created, managed, evaluated, and disseminated globally.

There is little doubt that the automated evaluation of translation will continue to play a key role in this emerging vision. However, a better understanding of the field’s de facto metrics and the broader MT evaluation process in this context is clearly imperative.

And what of use cases that continue to emerge, such as the possibility of intelligent or MT content in the educational space? The UTA research group is also exploring MT applications specific to education and higher education as well. For example, millions of users daily make use of learning materials such as MOOCs—educational content that attracts users across borders, languages, and cultures. A significant portion of international learners come to and potentially struggle with English-language content in edX or other MOOC courses—and thousands of MOOC offerings exist in the world’s languages, untranslated for English-speakers. What role might machine translation potentially play in this educational endeavor?

This is a more fleshed-out version of a blog post by Pete Smith and Henry Anderson of the University of Texas at Arlington already published on SDL.com. They describe initial results from a research project they are conducting on MT system quality measurement and related issues. 

Dr. Pete Smith, Chief Analytics Officer, and Professor

Mr. Henry Anderson, Data Scientist

Localization and Translation Program

Department of Modern Languages and Office of University Analytics

The University of Texas at Arlington

Most Popular Posts of 2016

This is a ranking of the most popular posts of 2016 on this blog according to Blogger, based on traffic and/or reader comment activity. Popular does not necessarily mean the best, and I have seen in the past that some posts that may not initially resonate, have real staying power and continue to be read actively years after the original publishing date, even though they are not initially popular. We can see from these rankings, that Neural MT certainly was an attention grabber for 2016, (even though I think for the business translation industry, Adaptive MT is a bigger game changer) and I look forward to seeing how NMT becomes more fitted to translation industry needs over the coming year.

I know with some certainty that the posts by Juan Rowda and Silvio Picinini will be read actively through the coming year and on, because they are not just current news that fades quickly like the Google NMT critique, but rather they are carefully gathered best practice knowledge that is useful as a reference over an extended period. These kinds of posts become long-term references that provide insight and guidance for others traversing a similar road or trying to build up task-specific expertise and wish to draw on best practices.

I have been much more active surveying the MT landscape since achieving my independent status, and I have a much better sense for the leading MT solutions now than I ever have. 

So here is the ranking of the most popular/active posts over the last 12 months.

The Google Neural Machine Translation Marketing Deception

This is a critique of the experimental process and related tremendous "success" reported by Google in making the somewhat outrageous claim that they had achieved "close to human translation" with their latest Neural MT technology. It is quite possible that the research team tried to rein in the hyperbole but were unsuccessful and the marketing team ruled on how this would be presented to the world.

A Deep Dive into SYSTRAN’s Neural Machine Translation (NMT) Technology

This is a report of a detailed interview with the SYSTRAN NMT team on their emergent neural MT technology. This was the first commercial vendor NMT solution available this last year and the continued progress looks very promising.

The Machine Translation Year in Review & Outlook for 2017

This was an annual wrap-up of the year in MT. I was surprised by how actively this was shared and distributed and at the time of this post is still the top post as per the Google ranking system. The information in the post was originally done together as a webinar with Tony O'Dowd of KantanMT. It was also interesting for me as I did some research on how much MT is being used and found out that on any given day as much as 500+ Billion words a day are being passed through a public MT engine somewhere in the world.

 

Comparing MT Based Translation Errors with Human Translation Errors

This is a guest post by Silvio Picinini,  a Machine Translation Language Specialist at eBay. The MTLS role is one that I think we will see more of within leading-edge LSPs as it simply makes sense when you are trying to solve large-scale translation challenges. The problems this eBay team solves have a much bigger impact on creating and driving positive outcomes for large-scale machine translation projects. The MTLS focus and approach is equivalent to taking post-editing to a strategic level of importance i.e. understand the data and solve 100,000 potential problems before an actual post-editor ever sees the MT output.

5 Tools to Build Your Basic Machine Translation Toolkit 

This is another post from the MT Language Specialist team at eBay, by Juan Martín Fernández Rowda. This is a post that I expect will become a long-term reference article and I expect that it will be actively read even a year from now as it describes high-value tools that a linguist should consider when involved with large or massive scale translation project where MT is the only viable option. His other contributions are also very useful references and worth close reading.

Private Equity, The Translation Industry & Lionbridge

This is yet another guest post, this time jointly with Luigi Muzii, that rapidly rose and gained visibility, as it provided some deeper analysis, and hopefully a better understanding of why private equity firms have focused so hard on the professional translation industry. There is a superficial reaction by many in the industry that seems to interpret this investment interest by PE firms as being so bullish on "translation," that they are interested in funding expansion plans at lackluster LSP candidates.  A deeper examination, suggests that the investment clearly is not just to give money to the firms they invest in, but it appears that many large LSPs are good "business turnaround and improve" candidates. This suggests that one of these "improved" PE LSP investments could become a real trailblazer in terms of re-defining the business translation value equation, and begin a evolutionary process whereby many marginal LSPs could be driven out of the market. However, we have yet to see even small signs of real success by any of the PE supervised firms thus far in changing and upgrading the market dynamics.

Comparing Neural MT, SMT and RBMT – The SYSTRAN Perspective

This is the result of an interesting interview with Jean Senellart, CEO and CTO at SYSTRAN, who is unique in the MT industry as being one of a handful of people who has deep exposure with all the current MT technology methodologies. In my conversations with Jean, I realized that he is one of the few people around in the "MT industry", who has deep knowledge and production-use experience with all three MT paradigms (RBMT, SMT, and NMT). There is a detailed article that describes the differences between these approaches on the SYSTRAN website for those who want more technical information.

Jean Senellart, CEO, SYSTRAN SA

 

10 Myths About Computer-Assisted Translation

This was a guest post by Vladimir “Vova” Zakharov, the Head of Community at SmartCAT. It examines some of the most widely held misconceptions about computer-assisted translation (CAT) technology. SmartCAT is a very interesting new translation process management tool, that is free to use, and takes collaboration to a much higher level than I have seen with most other TMS systems. And interestingly, this post was also very popular in Russia.

The single most frequently read post I have written thus far, is one that focuses on post-editing compensation. It was written in early 2012, but to this day, it still gets at least 1,000 views a month. This, I suppose shows that the industry has not solved basic problems, and I noticed that I am still talking about this issue in my outlook on MT in 2017. It remains an issue that many have said needs a better problem resolution. Let's hope that we can develop much more robust approaches to this problem this year. As I have stated before, there is an opportunity for industry collaboration to develop and share actual work related data to develop more trusted measurements. If multiple agencies collaborate and share MT and PEMT experience data we could get to a point where the measurements are much more meaningful and consistent across agencies.

Exploring Issues Related to Post-Editing MT Compensation