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Monday, August 5, 2019

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 na├»ve 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 system 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.
This situation will now change and continue to evolve with the innovative new NMT adaptation solution being introduced by SDL which is a hybrid of the MT vendor and DIY approach. A solution that provides the best of both.


 

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 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.

If you'd like to learn more about what's new in SDL's Adaptable Neural Language Pairs, click here.

Monday, June 17, 2019

The Challenge of Open Source MT

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

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.”
 
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.


Friday, April 26, 2019

Understanding MT Quality - What Really Matters?

This is the second post in our posts series on machine translation quality. Again this is a slightly less polished and raw variant of a version published on the SDL site. The first one focused on BLEU scores, which are often improperly used to make decisions on inferred MT quality, where it clearly is not the best metric to draw this inference.

The reality of many of these comparisons today is that scores based on publicly available (i.e. not blind) news domain tests are being used by many companies and LSPs to select MT systems which translate IT, customer support, pharma, financial services domain related content. Clearly, this can only result in sub-optimal choices.

The use of machine translation (MT) in the translation industry has historically been heavily focused on localization use cases, with the primary intention to improve efficiency, that is, speed up turnaround and reduce unit word cost. Indeed, machine translation post-editing (MTPE) has been instrumental in helping localization workflows achieve higher levels of productivity.




Many users in the localization industry select their MT technology based on two primary criteria:
  1. Lowest cost
  2. “Best quality” assessments based on metrics like BLEU, Lepor or TER, usually done by a third party
The most common way to assess the quality of an MT system output is to use a string-matching algorithm score like BLEU. As we pointed out previously, equating a string-match score with the potential future translation quality of an MT system in a new domain is unwise, and quite likely to result in disappointing results. BLEU and other string-matching scores offer the most value to research teams building and testing MT systems. When we further consider that scores based on old news domain content are being used to select systems for customer support content in IT and software subject domains it seems doubly foolish.

One problem with using news domain content is that it tends to lack tone and emotion. News stories discuss terrorism and new commercial ventures in almost exactly the same tone.  As Pete Smith points out in the webinar link below, in business communication, and customer service and support scenarios the tone really matters. Enterprises that can identify dissatisfied customers and address the issues that cause dissatisfaction are likely to be more successful. CX is all about tone and emotion in addition to the basic literal translation. 

Many users consider only the results of comparative evaluations – often performed by means of questionable protocols and processes using test data that is invisible or not properly defined – to select which MT systems to adopt.  Most frequently, such analyses produce a score table like the one shown below, which might lead users to believe they are using the “best-of-breed” MT solution since they selected the “top” vendor (highlighted in green). 

English to French
English to Chinese
English to Dutch
Vendor A – 46.5
Vendor C – 36.9
Vendor B – 39.5
Vendor B – 45.2
Vendor A – 34.5
Vendor C – 37.7
Vendor C – 43.5
Vendor B – 32.7
Vendor A – 35.5

While this approach looks logical at one level, it often introduces errors and undermines efficiency because of the administrative inconsistency between different MT systems. Also, the suitability of the MT output for post editing may be a key requirement for localization use cases, but this may be much less important in other enterprise use cases.




Assessing business value and impact


The first post in this blog series exposes many of the fallacies of automated metrics that use string-matching algorithms (like BLEU and Lepor), which are not reliable quality assessment techniques as they only reflect the calculated precision and recall characteristics of text matches in a single test set, on material that is usually unrelated to the enterprise domain of interest. 

The issues discussed challenge the notion that single-point scores can really tell you enough about long-term MT quality implications. This is especially true as we move away from the localization use case. Speed, overall agility and responsiveness and integration into customer experience related data flow matters much more in the following use cases. The actual translation quality variance measured by BLEU and Lepor may have little to no impact on what really matters in the following use cases.



The enterprise value-equation is much more complex and goes far beyond linguistic quality and Natural Language Processing (NLP) scores. To truly reflect the business value and impact, evaluation of MT technology must factor in non-linguistic attributes including:
  • Adaptability to business use cases
  • Manageability
  • Integration into enterprise infrastructure
  • Deployment flexibility   
To effectively link MT output to business value implications, we need to understand that although linguistic precision is an important factor, it often has a lower priority in high-value business use cases. This view will hopefully take hold as the purpose and use of MT is better understood in the context of a larger business impact scenario, beyond localization.

But what would more dynamic and informed approaches look like? MT evaluation certainly cannot be static since systems must evolve as requirements change. Instead of a single-point score, we need a more complex framework that provides an easy, single measure that tells us everything we need to know about an MT system. Today, this is unfortunately not yet feasible.




A more meaningful evaluation framework


While single-point scores do provide a rough and dirty sense of an MT system’s performance, it is more useful to focus testing efforts on specific enterprise use case requirements. This is also true for automated metrics, which means that scores based on news domain tests should be viewed with care since they are not likely to be representative of performance on specialized enterprise content. 

When rating different MT systems, it is essential to score key requirements for enterprise use, including:

  • Adaptability: Range of options and controls available to tune the MT system performance for very specific use cases. For example, optimization techniques applied to eCommerce catalog content should be very different from those applied to technical support chatbot content or multilingual corporate email systems.
  • Data privacy and security: If an MT system will be used to translate confidential emails, business strategy and tactics documents, human evaluation requirements will differ greatly from a system that only focuses on product documentation. Some systems will harvest data for machine learning purposes, and it is important to understand this upfront.
  • Deployment flexibility: Some MT systems need to be deployed on-premises to meet legal requirements, such as is the case in litigation scenarios or when handling high-security data. 
  • Expert services: Having highly qualified experts to assist in the MT system tuning and customization can be critical for certain customers to develop ideal systems. 
  • IT integration: Increasingly, MT systems are embedded in larger business workflows to enable greater multilingual capabilities, for example, in communication and collaboration software infrastructures like email, chat and CMS systems.
  • Overall flexibility: Together, all these elements provide flexibility to tune the MT technology to specific use cases and develop successful solutions.

Ultimately, the most meaningful measures of MT success are directly linked to business outcomes and use cases. The definition of success varies by the use case, but most often, linguistic accuracy as an expression of translation quality is secondary to other measures of success. 


The integrity of the overall solution likely has much more impact than the MT output quality in the traditional sense: not surprisingly, MT output quality could vary by as much as 10-20% on either side of the current BLEU score without impacting the true business outcome. Linguistic quality matters but is not the ultimate driver of successful business outcomes. In fact, there are reports of improvements in output quality in an eCommerce use case that actually reduced the conversion rates on the post-edited sections, as this post-edited content was viewed as being potentially advertising-driven and thus less authentic and trustworthy.








True expressions of successful business outcomes for different use cases


Global enterprise communication and collaboration
  • Increased volume in cross-language internal communication and knowledge sharing with safeguarded security and privacy
  • Better monitoring and understanding of global customers 
  • Rapid resolution of global customer problems, measured by volume and degree of engagement
  • More active customer and partner communications and information sharing
Customer service and support
  • Higher volume of successful self-service across the globe
  • Easy and quick access to multilingual support content 
  • Increased customer satisfaction across the globe
  • The ability of monolingual live agents to service global customers regardless of the originating customer’s language 
eCommerce
  • Measurably increased traffic drawn by new language content
  • Successful conversions in all markets
  • Transactions are driven by newly translated content
  • The stickiness of new visitors in new language geographies
Social media analysis
  • Ability to identify key brand impressions 
  • Easy identification of key themes and issues
  • A clear understanding of key positive and negative reactions
Localization
  • Faster turnaround for all MT-based projects
  • Lower production cost as a reflection of lower cost per word
  • Better MTPE experience based on post-editor ratings
  • Adaptability and continuous improvement of the MT system

A more detailed presentation and webinar that goes into much more detail on this subject is available from Brightalk. 


In upcoming posts in this series, we will continue to explore the issue of MT quality assessment from a broad enterprise needs perspective. More informed practices will result in better outcomes and significantly improved MT deployments that leverage the core business mission to solve high-volume multilingual challenges more effectively.