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.

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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

Adapting Neural MT to Support Digital Transformation

We live in an era where the issue of digital transformation is increasingly recognized as a primary concern, and a key focus of executive management teams in global enterprises. The stakes are high for businesses that fail to embrace change. Since 2000, almost half (52%) of Fortune 500 companies have either gone bankrupt, been acquired, or ceased to exist as a result of digital disruption. It’s also estimated that 75% of today’s S&P 500 will be replaced by 2027, according to Innosight Research.

Responding effectively to the realities of the digital world have now become a matter of survival as well a means to build long term competitive advantage.

When we consider what is needed to drive digital transformation in addition to structural integration, we see that large volumes of current, relevant, and accurate content that support the buyer and customer journey are critical to enhancing the digital experience both in B2C and B2B scenarios. 

Large volumes of relevant content are needed to enhance the customer experience in the modern digital era, where customers interact continuously with enterprises in a digital space, on a variety of digital platforms. To be digitally relevant in this environment requires that enterprises must increasingly be omni-market-focused, and have large volumes of relevant content available in every language in every market they participate on a continuous basis.

This requires that the modern enterprise must create more content, translate more content and deliver more content on an ongoing basis to be digitally relevant and visible. Traditional approaches to translating enterprise content simply cannot scale and a new approach is needed. The possibility of addressing these translation challenges without automation is nil, but what is required is a much more active man-machine collaboration that we at SDL call machine-first human optimized. Thus, the need for a global enterprise to escalate the focus on machine translation (MT) is growing and has become much more urgent. 

However, the days of only using generic MT to solve any high volume content translation challenges are over, and the ability of the enterprise to utilize MT in a much more optimal and agile manner across a range of different use cases is needed to enable an effective omni-market strategy to be deployed.

 A one-size-fits-all MT strategy will not enable the modern enterprise to effectively deliver the critical content needed to their target global markets in an effective and optimal way. 

Superior MT deployment requires ongoing and continuous adaptation of the core MT technology to varied use cases, subject domain, and customer-relevant content needs. MT deployment also needs to happen with speed and agility to deliver business advantage, and few enterprises can afford the long learning and development timelines required by any do-it-yourself initiative.

The MT Adaptation Challenge

Neural machine translation (NMT) has quickly established itself as the preferred model for most MT use cases today. Most experts now realize that MT performs best in industrial deployment scenarios when it is adapted and customized to the specific subject domain, terminology, and use case requirements. Generic MT is often not enough to meet key business objectives. However, the constraints to successful development of adapted NMT models is difficult for the following reasons:

1. The sheer volume of training data that is required to build robust systems. This is typically in the hundreds of millions of words range that few enterprises will ever be able to accumulate and maintain. Models built with inadequate foundational data are sure to perform poorly and fail in meeting business objectives and providing business value. Many AI initiatives fail or underperform because of data insufficiency. 
2. The available options to train NMT systems are complex and almost all of them require that any training data used to adapt NMT systems be made universally available to the development platform being used to further enhance their platform. This often raises serious data security and data privacy issues in this massively digital era, where the data related to the most confidential customer interactions and product development initiatives are needed to be translated on a daily basis. Customer interactions, sentiment and service, and support data are too valuable to be shared with open source AI platforms.
3. The cost of keeping abreast of state-of-the-art NMT technology standards are also high. For example, a current best of breed English to German NMT system requires tens of millions of training data segments, hundreds and even thousands of hours of GPU cycles, deep expertise to tune and adjust model parameters and knowhow to bring it all together. It is estimated that just for this one single system it costs around $9,000 in training time costs on public cloud infrastructure, and 40 days of training time! These costs are likely to be higher if the developer does not have real expertise and is learning as they attempt to do it. These costs can be reduced substantially by moving to an on-premise training setup and by working with a foundation sytem that has been set up by experts.
4. NMT model development requires constant iteration and ongoing and continuous experimentation with varying data sets and tuning strategies. There is a certain amount of uncertainty in any model development and outcomes cannot always be predicted upfront thus repeated and frequent updates should be expected. Thus, computing costs can rapidly escalate when using cloud infrastructure. 

Given the buzz around NMT, many naïve practitioners jump into DIY (do-it-yourself) open-source options that are freely available, only to realize months or years later that they have nothing to show for their efforts. 

The many challenges of working with open-source NMT are covered here. While it is possible to succeed with open-source NMT, a sustained and ongoing research/production investment is required with very specialized human resources to have any meaningful chance of success.

The other option that enterprises employ to meet their NMT adaptation needs is to go to dedicated MT specialists and MT vendors, and there are significant costs associated with this approach as well. The ongoing updates and improvements usually come with direct costs associated with each individual effort. These challenges have limited the number of adapted and tuned NMT systems that can be deployed, and have also created resistance to deploying NMT systems more widely as generic system problems are identified.

The most informed practitioners are just beginning to realize that using BLEU scores to select MT systems is usually quite short-sighted. The business impact of 5 BLEU points this way or that is negligible in most high value business use cases, and use case optimization is usually much more beneficial and valuable to the business mission.

As a technology provider who is focused on enterprise MT needs, SDL already provides existing adaptation capabilities, which range from:

• Customer created dictionaries for instant self-service customization – suitable for specific terminology enforcement on top of a generic model. 
• NMT model adaptation as a service, performed by the SDL MT R&D team.

 

The Innovative SDL NMT Adaptation Solution

The SDL NMT Trainer solution provides the following:

• Straightforward and simple NMT model adaptation without requiring users to be data scientists or experts.
• Foundational data provided in the Adaptable Language Pairs to expedite and accelerate the development of robust and deployable systems quickly.
• On-premise training that completely precludes the possibility of any highly confidential training data that encapsulates customer interactions, information governance, product development and partner and employee communications ever leaving the enterprise.
• Once created the encrypted adapted models can be deployed easily on SDL in an on-premise deployment or cloud infrastructure with no possibility of data leakage.
• Multiple use cases and optimizations are possible to be developed on a single language pair and customers can re-train and adjust their models continuously as data becomes available or as new use cases are identified.
• A pricing model that encourages and supports continuous improvement and experimentation on existing models and allows for many more use cases to be deployed on the same language combination. 

The initial release of the SDL On-Premise Trainer is expected to be the foundation of an ever-adapting machine translation solution that will grow in capability and continue to evolve with additional new features.

Research shows that NMT models are very dependent on high quality training data and outcomes are highly dependent on the quality of the data used. The cleaner the data is, the better the adaptation will be, and thus after this initial product release, SDL plans to introduce an update later this year that leverages years of experience in translation memory management to include the appropriate automated cleaning steps required to make the data used as good as possible for neural MT model training.

The promise of the best AI solutions in the market is to continuously learn and improve with informed and structured human feedback, and the SDL technology is being architected to evolve and improve with this human feedback. While generic MT serves the needs of many internet users who need to get a rough gist of foreign language content, the global enterprise needs MT solutions that perform optimally on critical terminology, and are sensitive to linguistic requirements within the enterprise’s core subject domain. This is a solution that leverages a customer’s ability to produce high quality adaptations with minimal effort in as short a time as possible and thus make increasing volumes of critical DX content multilingual.

The Challenge of Open Source MT

MT is considered one of the most difficult problems in the general AI and machine learning field. In the field of artificial intelligence, the most difficult problems are informally known as AI-complete problems, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem— that is, making computers as intelligent as people. It is no surprise that humankind has been working on this problem for almost 70 years now, and is still quite some distance from having solved this problem.

“To translate accurately, a machine must be able to understand the text. It must be able to follow the author's argument, so it must have some ability to reason. It must have extensive world knowledge so that it knows what is being discussed — it must at least be familiar with all the same commonsense facts that the average human translator knows. Some of this knowledge is in the form of facts that can be explicitly represented, but some knowledge is unconscious and closely tied to the human body: for example, the machine may need to understand how an ocean makes one feel to accurately translate a specific metaphor in the text. It must also model the authors' goals, intentions, and emotional states to accurately reproduce them in a new language. In short, the machine is required to have a wide variety of human intellectual skills, including reason, commonsense knowledge and the intuitions that underlie motion and manipulation, perception, and social intelligence. Machine translation, therefore, is believed to be AI-complete.”

Excerpt from Wikipedia

 
One of the myths that seem to prevail in the localization world today is that anybody with a hoard of translation memory data can easily develop and stand-up an MT system using one of the many open source toolkits or DIY (do-it-yourself) solutions that are available. We live in a time where there is a proliferation of open source machine learning and AI related development platforms. Thus, people believe that given some data, and a few computers, a functional and useful MT system can be developed. However, as many who have tried have found out, the reality is much more complicated and the path to success is long, winding, and sometimes even treacherous. For an organization to successfully consider developing an open source machine translation solution to deployable quality, a few critical elements for successful outcomes is required:

1. At least a basic competence with machine learning technology, 
2. An understanding of the broad range of data needed and used in building and developing an MT system,
3. An understanding of the proper data preparation and data optimization processes needed to maximize success,
4. The ability to understand, measure and respond to successful and failure outcomes with model building that are very much part of the development process,
5. An understanding of the additional support tools and connected data flow infrastructure needed to make MT deployable at enterprise scale.

The very large majority of open source MT efforts fail, in that they do not consistently produce output that is equal to, or better than, any easily accessed public MT solution, or they cannot be deployed in a robust and effective manner. 

This is not to say that this is not possible, but the investments and long-term commitment required for success are often underestimated or simply not properly understood. A case can always be made for private systems that offer greater control and security, even if they are generally less accurate than public MT options. However, in the localization industry, we see that if “free” MT solutions are available that are superior to an LSP built system, translators will prefer to just use those. We also find that for the few of these self-developed MT systems that do produce useful output quality, larger integration and data integration issues are often an impediment, and thus difficult to deploy at enterprise scale and robustness. 

Some say that those who ignore the lessons of history are doomed to repeat the errors. Not so long ago, when the Moses SMT toolkits were released, we heard industry leaders’ claim, “Let a thousand MT systems bloom”, but in retrospect, did more than a handful survive beyond the experimentation phase?

Why is relying on open source difficult for enterprise use?

The state-of-the-art of machine translation and the basic technology is continuously evolving and practitioners need to understand and stay current with the research to have viable systems in deployment. A long, sustained and steady commitment is needed just to stay abreast.

If public MT can easily outperform home-built systems, there is little incentive for employees and partners to use these in-house systems, and thus we are likely to see rogue behavior where users will reject the in-house system, or see users forced to use sub-standard systems. This is especially true for MT systems in localization use cases where the highest output quality is demanded. Producing systems that consistently perform as required, needs deep expertise and broad experience. An often overlooked reason for failure is that to do it yourself, it is necessary to have an understanding and some basic expertise with the various elements in and around machine learning technology. Many do-it-yourselfers don’t know how to do any more than load TM into an open source framework.

While open source does indeed provide access to the same algorithms, much of the real skill in building MT systems is in the data analysis, data preparation, and data cleansing to ensure that the algorithms learn from a sound quality foundation. The most skillful developers also understand the unique requirements of different use cases and may develop additional tools and processes to augment and enhance the MT related tasks. Often times the heavy lifting for many uses cases is done outside and around the neural MT models, understanding error patterns and developing strategies to resolve them.

Staying abreast is a challenge

Over the last few years, the understanding of what the “best NMT algorithms” are has changed regularly. A machine translation system that is deployed on an enterprise scale requires an “all in” long-term commitment or it will be doomed to be a failed experiment:

• Building engineering teams that understand what research is most valid and relevant, and then upgrading and refreshing existing systems is a significant, ongoing and long-term investment. 
• Keeping up with the evolution in the research community requires constant experimentation and testing that most practitioners will find hard to justify. 
• Practitioners must know why and when to change as the technology evolves or risk being stuck with sub-optimal systems. 

Open-source initiatives that emerge in academic environments, such as Moses, also face challenges. They often stagnate when the key students that were involved in setting up initial toolkits graduate and are hired away. The key research team may also move on to other research that has more academic stature and potential. These shifting priorities can force DIY MT practitioners to switch toolkits at great expense, both in terms of time and redundant resource expenditures.

 

To better understand the issue of a basic open-source MT toolkit in the face of enterprise MT capability requirements, consider why an organization would choose to use an enterprise-grade content management system (CMS) to set up a corporate website instead of a tool like WordPress. While both systems could be useful in helping the organization build and deploy a corporate web presence, enterprise CMS systems are likely to offer specialized capabilities that make them much more suitable for enterprise use.

 

 

Deep expertise with MT is acquired over time by building thousands of systems across varied use cases and language combinations. Do we really believe that a DIY practitioner who builds a few dozen systems will have the same insight and expertise? Expertise and insight are acquired painstakingly over time. It is very easy "to do MT badly" and quite challenging to do it well.

 As the global communication, collaboration and content sharing imperatives demanded by modern digital transformation initiatives become well understood, many enterprises see that MT is now a critical technology building block that enables better DX. However, there are many specialized requirements including data security and confidentiality, adaptation to different business use cases, and the ability to deploy systems in a broad range of enterprise use scenarios. MT is increasingly a mission-critical technology for global business and requires the same care and attention that the selection of enterprise CMS, email, and database systems do. The issue of enterprise optimization is an increasingly critical element in selecting this kind of core technology.

What are the key requirements for enterprise MT?

There is more to successful MT deployment than simply being able to build an NMT model. A key requirement for successful MT development by the enterprise is long-term experience with machine learning research and technology at industrial scale in the enterprise use context.

With MT, actual business use case experience also matters since it is a technology that requires the combination of computational linguistics, data management, human translator interaction, and systems integration into organizational IT infrastructure for robust solutions to be developed. Best practices evolve from extensive and broad experience that typically takes years to acquire, in addition to success with hundreds, if not thousands, of systems.

The SDL MT engineering team has been a pioneer on data-driven MT technology since its inception with Statistical MT in the early 2000s and has been involved with a broad range of enterprise deployments in the public and private sectors. The deep expertise that SDL has built since then encompasses the combined knowledge gained in all of the following areas:

• Data preparation for training and building MT engines, acquired through the experience of building thousands of engines across many language combinations for various use cases.
• Deep machine learning techniques to assess and understand the most useful and relevant research in the NLP community for the enterprise context.
• Development of tools and architectural infrastructure that allows rapid adoption of research breakthroughs, but still maintains existing capabilities in widely deployed systems.
• Productization of breakthrough research for mission-critical deployability, which is a very different process from typical experimentation.
• Pre- and post-processing infrastructure, tools and specialized capabilities that add value around core MT algorithms and enable systems to perform optimally in enterprise deployment settings. 
• Ongoing research to adapt MT research for optimal enterprise use, e.g., using CPUs rather than GPUs to reduce deployment costs, as well as the system cost and footprint. 
• Long-term efforts on data collection, cleaning, and optimization for rapid integration and testing with new algorithmic ideas that may emerge from the research community.
• Close collaboration with translators and linguists to identify and solve language-specific issues, which enables unique processes to be developed to solve unique problems around closely-related languages. 
• Ongoing interaction with translators and informed linguistic feedback on error patterns provide valuable information to drive ongoing improvements in the core technology.
• Development of unique language combinations with very limited data availability (e.g., ZH to DE) by maximizing the impact of available data. Utilization of zero-shot translation (between language pairs the MT system has never seen) produces very low-quality systems through its very basic interlingua, but can be augmented and improved by intelligent and informed data supplementation strategies.
• Integration with translation management software and processes to allow richer processing by linguistic support staff.
• Integration with other content management and communication infrastructure to allow pervasive and secure implementation of MT capabilities in all text-rich software infrastructure and analysis tools.

The bottom line

The evidence suggests that embarking on a self-managed open-source-based MT initiative is for the very few who are ready to make the substantial long-term commitment and investments needed. Successful outcomes require investment in building expertise not only in machine learning but in many other related and connected areas. The same kinds of rules that apply to enterprise decisions on selecting email, content management and database systems should apply here. Properly executed, MT is a critical tool that enhances and expands the digital global footprint of the organization, and it should be treated with the same seriousness dedicated to any major strategic initiative.

This is the raw, first draft, and slightly longer rambling version of a post already published on SDL.COM.