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Tuesday, October 8, 2019

Post-editese is real

Ever since machine translation was introduced into the professional translation industry, there have been questions about what the impact would be on a final delivered translation service product. For much of the history of MT many translators claimed that while translation production work using a post-edited MT (PEMT) process was faster, the final product was not as good. The research suggests that this has been true from a strictly linguistic perspective, but many of us also know that PEMT worked quite successfully with technical content especially with terminology and consistency even in the days of SMT and RBMT. 

As NMT systems proliferate, we are at a turning point, and I suspect that we will see many more NMT systems that are in fact seen as providing useful output that clearly enhances translator productivity, especially on output from systems built by experts. NMT will also quite likely have an influence on the output quality and the difference is also likely to become less prominent. This is what is meant by developers who make claims of achieving human parity. If competent human translators cannot tell that segments they review came from MT or not, we can make a limited claim of having achieved human parity. This does not mean that this will be true for every new sentence submitted to this system. 

We should also understand that MT  provides the greatest value in use scenarios where you have large volumes of content (millions rather than thousands of words), short turnaround times, and limited budgets. Increasingly MT is used in scenarios where little or no post-editing is done, and by many informed estimates, we are already at a run rate of a trillion words a day going through MT engines. While post-editese may be an important consideration in localization use scenarios, this is likely no more than 2% of all MT usage.  

Enterprise MT use is rapidly moving into a phase where it is an enterprise-level IT resource. The modern global enterprise needs to enable and allow millions of words to be translated on demand in a secure and private way and needs to be integrated deeply into critical communication, collaboration, and content creation and management software.

The research presented by Antonio Toral below documents the impact of post-editing on the final output across multiple different language combinations and MT systems. 



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This is a summary of the paper “Post-editese: an Exacerbated Translationese” by Antonio Toral, which was presented at MT Summit 2019, where it won the best paper award.


Introduction


Post-editing (PE) is widely used in the translation industry, mainly because it leads to higher productivity than unaided human translation (HT). But, what about the resulting translation? Are PE translations as good as HT? Several research studies have looked at this in the past decade and there seems to be consensus: PE is as good as HT or even better (Koponen, 2016).

Most of these studies measure the quality of translations by counting the number of errors therein. Taking into account that there is more to quality than just the number of mistakes, we ask ourselves the following question instead: are there differences between translations produced with PE vs HT? In other words, does the final output created via PEs and HTs have different traits?

Previous studies have unveiled the existence of translationese, i.e. the fact that HTs and original texts exhibit different characteristics. These characteristics can be grouped along with the so-called translation universals (Baker, 1993) and fundamental laws of translation (Toury, 2012), namely simplification, normalization, explicitation and interference. Along this line of thinking, we aim to unveil the existence of post-editese (i.e. the fact that PEs and HTs exhibit different characteristics) by confronting PEs and HTs using a set of computational analyses that align to the aforementioned translation universals and laws of translation.

Data

We use three datasets in our experiments: Taraxü (Avramidis et al., 2014), IWSLT (Cettolo et al., 2015; Mauro et al., 2016) and Microsoft “Human Parity” (Hassan et al., 2018). These datasets cover five different translation directions and allow us to assess the effect of machine translation (MT) systems from 2011, 2015-16 and 2018 on the resulting PEs.

Analyses

Lexical Variety

We assess the lexical variety of a translation (HT, PE or MT) by calculating its type-token ratio:

In other words, given two translations equally long (number of words), the one with bigger vocabulary (higher number of unique words) would have a higher TTR, being therefore considered lexical richer, or higher in lexical variety.

The following figure shows the results for the Microsoft dataset for the direction Chinese-to-English (zh–en, the results for the other datasets follow similar trends and can be found in the paper). HT has the highest lexical variety, followed by PE, while the lowest value is obtained by the MT systems. A possible interpretation is as follows: (i) lexical variety is low in MT because these systems prefer the translation solutions that are frequent in the training data used to train such systems and (ii) a post-editor will add lexical variety to some degree (difference in the figure between MT and PE), but because MT primes him/her (Green et al., 2013), the resulting PE translation will not achieve the lexical variety of HT.


Lexical Density

The lexical density of a text indicates its amount of information and is calculated as follows:
where content words correspond to adverbs, adjectives, nouns, and verbs. Hence, given two translations equally long, the one with the higher number of content words would be considered to have higher lexical density, in other words, to contain more information.

The following figure shows the results for the three translation directions in the Taraxü dataset: English-to-German, German-to-English and Spanish-to-German. The lexical density in HT is higher than in both PE and MT and there is no systematic difference between the latter two.

Length Ratio

Given a source text (ST) and a target text (TT), where TT is a translation of ST (HT, PE or MT), we compute a measure of how different in length the TT is with respect to the ST:
This means that the bigger the difference in length between the ST and the TT (be it because TT is shorter or longer than the ST), the higher the length ratio.

The following figure shows the results for the Taraxü dataset. The trend is similar to the one in lexical variety; this is, HT obtains the highest result, MT the lowest and PE lies somewhere in between. We interpret this as follows: (i) MT results in a translation of similar length to that of the ST due to how the underlying MT technology works and PE is primed by the MT output while (ii) a translator working from scratch may translate more freely in terms of length.

Part-of-speech Sequences

Finally, we assess the interference of the source language on a translation (HT, PE and MT) by measuring how close the sequence of part-of-speech tags in the translation is to the typical part-of-speech sequences of the source language and to the typical part-of-speech sequences of the target language. If the sequences of a translation are similar to the typical sequences of the source language that would indicate that there is an inference from the source language in the translation.

The following figure shows the results for the IWSLT dataset. The metric used is perplexity difference; the higher it is the lower the interference (full details on the metric can be found in the paper). Again, we find a similar trend as in some of the previous analyses: HT gets the highest results, MT the lowest and PE somewhere in between. The interpretation is again similar: MT outputs exhibit a large amount of interference from the source language, a post-editor gets rid of some of that interference but the resulting translation still has more interference than an unaided translation.


Findings

The findings from our analyses can be summarised as follows in terms of HT vs PE:
  • PEs have lower lexical variety and lower lexical density than HTs. We link these to the simplification principle of translationese. Thus, these results indicate that post-editese is lexically simpler than translationese.
  • Sentence length in PEs is more similar to the sentence length of the source texts, than sentence length in HTs. We link this finding to interference and normalization: (i) PEs have
interference from the source text in terms of length, which leads to translations that follow the typical sentence length of the source language; (ii) this results in a target text whose
length tends to become normalized.
  • Part-of-speech (PoS) sequences in PEs are more similar to the typical PoS sequences of the source language than PoS sequences in HTs. We link this to the interference principle: the sequences of grammatical units in PEs preserve to some extent the sequences that are typical of the source language.

In terms of the role of MT: we have not considered only HTs and PEs but also MT outputs, from the MT systems that were the starting point to produce the PEs. This to corroborate a claim in the literature (Greenet al., 2013), namely that in PE the translator is primed by the MT output. We expected then to find similar trends to those found in PEs also in MT outputs and this was indeed the case in all four analyses. In some experiments, the results of PE were somewhere in between those of HT and MT. Our interpretation is that a post-editor improves the initial MT output, but due to being primed by the MT output, the result cannot attain the level of HT, and the footprint of the MT system remains in the resulting PE.

Discussion

As said in the introduction, we know that PE is faster than HT. The question I wanted to address was then: can PE not only be faster but also be at the level of HT quality-wise? In this study, this is looked at from the point of view of translation universals and the answer is clear: no. However, I'd like to point out three additional elements:
  1. The text types in the 3 datasets that I have used are news and subtitles, both are open-domain and could be considered to a certain extent "creative". I wonder what happens with technical texts, given their relevance for industry, and I plan to look at that in the future.
  2. As mentioned in the introduction, previous studies have compared HT vs PE in terms of the number of errors in the resulting translation. In all the studies I've encountered PE is at the level of HT or even better. Thus, for technical texts where terminology and consistency are important, PE is probably better than HT. I find thus the choice between PE and HT to be a trade-off between consistency on one hand and translation universals (simplification, normalization and interference) on the other.
  3. PE falls behind HT in terms of translation universals because MT falls behind HT in those terms. However, this may not be the case anymore in the future. For example, the paper shows that PE-NMT has less interference than PE-SMT, thanks to the better reordering in the former.




Antonio Toral is an Assistant Professor at the Computational Linguistics group, Center for Language and Cognition, Faculty of Arts, University of Groningen (The Netherlands). His research is in the area of Machine Translation. His main topics include resource acquisition, domain adaptation, diagnostic evaluation and hybrid approaches.


Related Work

Other work has previously looked at HT vs PE beyond the number of errors. The most related papers to this paper are Bangalore et al. (2015), Carl and Schaeffer (2017), Czulo and Nitzke (2016), Daems et al. (2017) and Farrell (2018).

Bibliography


Avramidis, Eleftherios, Aljoscha Burchardt, Sabine Hunsicker, Maja Popovic, Cindy Tscherwinka, David Vilar, and Hans Uszkoreit. 2014. The taraxü corpus of human-annotated machine translations. In LREC, pages 2679–2682.

Baker, Mona. 1993. Corpus linguistics and translation studies: Implications and applications. Text and technology: In honor of John Sinclair, 233:250.

Bangalore, Srinivas, Bergljot Behrens, Michael Carl, Maheshwar Gankhot, Arndt Heilmann, Jean Nitzke, Moritz Schaeffer, and Annegret Sturm. 2015. The role of syntactic variation in translation and post-editing. Translation Spaces, 4(1):119–144.

Carl, Michael and Moritz Jonas Schaeffer. 2017. Why translation is difficult: A corpus-based study of non-literality in post-editing and from-scratch translation. Hermes, 56:43–57.

Cettolo, Mauro, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Roldano Cattoni, and Marcello Federico. 2015. The iwslt 2015 evaluation campaign. In IWSLT 2015, International Workshop on Spoken Language Translation.

Green, Spence, Jeffrey Heer, and Christopher D Manning. 2013. The efficacy of human post-editing for language translation. Chi 2013, pages 439–448.

Hassan, Hany, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, Will Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Zhuangzi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, and Ming Zhou. 2018. Achieving Human Parity on Automatic Chinese to English News Translation. https://arxiv.org/abs/1803.05567.

Koponen, Maarit. 2016. Is machine translation post-editing worth the effort? A survey of research into post-editing and effort. Journal of Specialised Translation, 25(25):131–148.

Mauro, Cettolo, Niehues Jan, Stüker Sebastian, Bentivogli Luisa, Cattoni Roldano, and Federico Marcello. 2016. The iwslt 2016 evaluation campaign. In International Workshop on Spoken Language Translation.

Toury, Gideon. 2012. Descriptive translation studies and beyond: Revised edition, volume 100. John Benjamins Publishing.

Wednesday, September 25, 2019

In a Funk about BLEU

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. 

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 didnt turn upjust as expectednow 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?




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


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 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.
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.