Highlights from Recent Coverage on MT Related Subjects

This is a summary of what I think are some interesting recent articles on the web on subjects relating to MT.

The Big Wave, an Italian initiative that focuses on the changes happening in language technology released details and proceeding papers from their conference held in Rome in the summer of 2011. There are many interesting papers related to MT, controlled language and collaborative translation related issues. These papers provide a balance of practitioner, academic and user perspectives on these subjects and are worth a close examination.
Some highlights include:

Linguistic resources and MT trends for the Italian language by Isabella Chiari discusses the implications of various kinds of data and their value for building data-driven MT systems and provides some specifics for EN <> IT MT systems. The paper is a great overview on the kinds of data that can be used and also provides insight on what data to use and where to use it with summary implications. It also makes a great case for the inevitability of corpus driven approaches in MT (without meaning to) by providing the theoretical rationale for this and points to rising momentum of the data driven approach.

Productivity and quality in MT post-editing – by Ana Guerberof provides specific evidence of the productivity advantage of MT over TM and new segments in a translation workflow.

“In this context, it seems logical to think that if prices, quality and times are already established for TMs according to different level of fuzzy matches then we just need to compare MT segments with TM segments, rather than comparing MT to human translation. “

This study also helps to establish that in reality MT is just a new kind of TM fuzzy match. Even though the test only involved a small number of translators and a small amount of work, it was done with care to ensure the translators saw a mixture or MT, TM and new segments in a way that was “blind” and then carefully measured the productivity of the translators in processing these different segments.

 

The results show that MT had higher productivity than TM or New segments and that on average MT produced higher productivity. (We are certain these results would have been more pronounced with an Asia Online customized system). Interestingly this study also shows that weaker translators seem to benefit more from MT and TM than the “best” translators. There are some interesting observations about the error analysis which showed that TM produced the greatest amount of final errors.

 

I would hypothesize that a test with more translators in the pool, and a bigger set of test data would be useful to do, as the results would establish the benefits of the use of customized MT much more clearly. It may even be useful to include “bad” or free MT to show how differently translators react to a segment that looks like it is an 85% match and to one that looks obviously like raw free MT or instant customization (50% TM match) that some use today.

 

 Why Machine Translation Matters: Trends & Best Practices:  

This article summarizes the forces driving the increasing use of MT which can be summarized as:

External Forces in the World at Large :-

• The digital data explosion and its impact on new content that begs to be to translated quickly

• The global thirst for knowledge and information

• The growing online population that does not speak English or FIGS but represents a major commercial opportunity for global enterprises

Internal Forces affecting Global Enterprises :-

• The growing importance of customer conversations and user generated content which affects purchase decisions and impacts customer loyalty

• The growing importance of open collaboration in B2C relationships

• The Rise of Asia and BRICI which requires huge amounts of new content in new languages

These forces, together amount to a shift towards more dynamic content, and increase the need to handle streaming flows of information that simply cannot be done without more automation and MT.

 

MT: the new 'lingua franca' is a fascinating perspective by Nicholas Oster, a historian of world languages on how MT is enabling linguistic diversity on the Internet.

“Between 2000 and 2009, Arabic on the internet grew twentyfold, Chinese x20, Portuguese x9, Spanish X7 and French x6, while content in English ‘only’ tripled. Proportionally, then, English is declining in importance relatively quickly. “The main story of growth on the Internet … is of linguistic diversity, not concentration.”

Ostler sees a key role for MT in this new environment. Just as the print revolution changed the ‘ground rules of communication’ in 16th century Europe, he expects that language and translation technology will revolutionize global communications tomorrow, removing the need for a ‘single lingua franca for all who wish to participate directly in the main international conversation.’

Translation errors or nuances in both humans and computers can naturally have an important impact. But there is no point in dismissing MT by judging it by some presumed norm of ‘perfect’ human translation. MT is a revolutionary tool that can help the world communicate better. TAUS will be welcoming Nicholas Ostler as a speaker at the upcoming TAUS European Summit on May 31 – June 1 in Paris.

When Machine Translation Usefulness Is Higher Than Quality:  

This article provides some interesting feedback for those who insist that MT only has value when it approaches human quality, and since MT rarely reaches human quality it has very limited value. In this study, English news was translated into FIGS by MT, but users were always given access to the English source. The study measures the usefulness of the MT in the context of assessed translation quality as shown below and interestingly MT is considered useful even when the quality falls short of excellence. Since this study was performed some time ago we would assume that the usefulness curve continues to shift upwards, driven by improving MT quality, whatever some translators may think about the quality.

The graph shows that although the machine translation quality was evaluated as being far from perfect, the translation’s usefulness was regarded as higher than its quality. However, this applies only when translation quality is above certain threshold. Bad or poor quality machine translations are naturally deemed as useless.

 

This result confirms what many MT proponents have themselves experienced. Pure MT can be rough – often obscure, frequently humorous – but it can be useful. If one really has little facility in the source language, pure MT translations, however clumsy, can be a boon to understanding and, by extension, to productivity.

 

The graph below illustrates the breakdown of responses to the question, “How would you rate the overall quality of the newsletter translation?” by language group. Note that Germans felt the quality was more lacking, possibly because the MT was poorer in quality or possibly because they had higher expectations. It is actually well known in the MT community that German <> English is more difficult than English <> Romance languages.



When we segment answers to the question, “How would you rate the usefulness of the newsletter translation?” by the respondents’ English ability, we see an even stronger vote in favor of MT by the two lower groups. Thus users who had a self-measured poorer English ability, found the MT much more useful. In fact even many who responded has having “Good” English ability found the MT very useful or essential.



There have also been some interesting discussions in LinkedIn that cover the dialogue and tension between translators and MT advocates and also expose some of the hyperbole that some MT enthusiasts are prone to. While the discussion does meander between translator emotions about plans to “eliminate” them and less than scrupulous business practices by some MT vendors, it is an interesting thread. In their rush to get on the technology bandwagon some LSPs may overlook the privacy and data security issues that they inadvertently agree to when they use instant Moses and DIY kits.  So caveat emptor.


In Machine Translation in the European Union : Renato provides some summary coverage from  a recent conference of the ever expanding use of MT in the European Union internal administration.

 

Interview with Translator David Bellos: author and award-winning translator David Bellos knows a thing or two about translation would be an understatement. With over 40 years of experience, he has achieved international recognition for his works as a translator and biographer and has an impressive list of acclaimed publications to his name.

Some interesting excerpts from the interview:

“What I expect is that machines will allow the demand for translation to carry on growing, and for translation to become an ever more integral part of the world we live in.

However, since there are almost 49 million translation directions between all the languages in the world and there is never going to be a 49-million-fold community of translators, machines might well be a useful adjunct to actual translation for many of the under served directions that exist.

A Short Guide to Measuring and Comparing Machine Translation Engines

This is an article from the Asia Online November 2011 Newsletter that provides useful advice for meaningful comparisons of  MT engines and is authored by Dion Wiggins, CEO of Asia Online. So the next time somebody promises you a BLEU of 60, be skeptical, and make sure you get the proper context and assurances that it was properly done. And if they say they have a BLEU score of 90 you know that you are clearly in the bullshit zone.

“What is your BLEU score?” This is the single most irrelevant question relating to translation quality, yet one of the most frequently asked. BLEU scores and other translation quality metrics greatly depend on many factors that must be understood in order for a score to be meaningful. A BLEU score of 20 in some cases can be better than a BLEU score of 50 or vice versa. Without understanding how a test set was measured and other details such as language pair and domain complexity, a BLEU score without context is not much more than a meaningless number. (For a primer on BLEU look here.)

BLEU scores and other translation quality metrics will vary based upon:

• The test set being measured: Different test sets will give very different scores. A test set that is out of domain will usually score lower than a test set that is in the domain of the translation engine being tested. The quality of the segments in the test set should be gold standard (i.e. validated as correct by humans). Lower quality test set data will give a less meaningful score.
• How many human reference translations were used: If there is more than one human reference translation, the resulting BLEU score will be higher as there are more opportunities for the machine translation to match part of the reference.
• The complexity of the language pair: Spanish is a simpler language in terms of grammar and structure than Finnish or Chinese relative to English. Typically if the source or target language is relatively more complex,  the BLEU score will be lower.
• The complexity of the domain: A patent has far more complex text and structure than a children’s story book. Very different metric scores will be calculated based on the complexity of the domain. It is not practical to compare two different test sets and conclude that one translation engine is better than the other.
• The capitalization of the segments being measured: When comparing metrics, the most common form of measurement is Case Insensitive. However when publishing, Case Sensitive is also important and may also be measured.
• The measurement software: There are many measurement tools for translation quality. Each may vary slightly with respect to how a score is calculated, or the settings for the measure tools may not be set the same. The same measurement software should be used for all measurements. Asia Online provides Language Studio™ Pro free of charge and this software measures the scores, for a given test set, for a variety of quality metrics.

It is clear from the above list of variables that a BLEU score number by itself has no real meaning.

How BLEU scores and other translation metrics are measured

With BLEU scores, a higher score indicates higher quality. A BLEU score is not a linear metric. A 2 BLEU point increase from 20 to 22 will be considerably more noticeable than the same increase from 50 to 52. F-Measure and METEOR also work in this manner where a higher score is also better. For Translation Error Rate (TER), a lower score is a better score. Language Studio™ Pro supports all of these metrics and can be downloaded for free.

Basic Test Set Criteria Checklist

The criteria specified by this checklist are absolute. Not complying with any of the checklist items will result in a score that is unreliable and less meaningful.

• Test Set Data should be very high quality: If the test set data are of low quality, then the measurement delivered will not be reliable.
• Test set should be in domain: The test set should represent the type of information that you are going to translate. The domain, writing style and vocabulary should be representative of what you intend to translate. Testing on out-of-domain text will not result in a useful metric.
• Test Set Data must not be included in the training Data: If you are creating an SMT engine, then you must make sure that the data you are testing with or very similar data are not in the data that the engine was trained with. If the test data are in the training data the scores will be artificially high and will not represent the level of quality that will be output when other "blind" data are translated.
• Test Set Data should be data that can be translated: Test set segments should have a minimal amount of dates, times, numbers and names. While a valid part a segment, they are not parts of the segment that are translated; they are usually transformed or mapped. The focus for a test set should be on words that are to be translated.
• Test Set Data should have segments that are at between 8 and 15 words in length: Short segments will artificially raise the quality scores as most metrics do not take into account segment length. Short segments are more likely to get a perfect match of the entire phrase, which is not a translation and is more like 100% match with a translation memory. The longer the segment, the more opportunity there is for variations on what is being translated. This will result in artificially lower scores, even if the translation is good. A small number of segments shorter than 8 words or longer than 15 words are acceptable, but these should be limited.
• Test set should be at least 1,000 segments: While it is possible to get a metric from shorter test sets, a reasonable statistic representation of the metric can only be created when there are sufficient segments to build statistics from. When there are only a low number of segments, small anomalies in one or two segments can raise or reduce the test set score artificially.Be skeptical of scores from test sets that only contain a few hundred sentences.

Comparing Translation Engines - Initial Assessment Checklist

Language Studio™ can be used for calculating BLEU, TER, F-Measure and METEOR scores.

• All conditions of the Basic Test Set Criteria must be met: If any condition is not met, then the results of the test could be flawed and not meaningful or reliable.
Test set must be consistent: The exact same test set must be used for comparison across all translation engines. Do not use different test sets for different engines.
• Test sets should be “blind”: If the MT engine has seen the test set before or included the test set data in the training data, then the quality of the output will be artificially high and not represent the true quality of the system.
• Tests must be carried out transparently: Where possible, submit the data yourself to the MT engine and get it back immediately. Do not rely on a third party to submit the data. If there are no tools or APIs for test set submission, the test set should be returned within 10 minutes of being submitted to the vendor via email. This removes any possibility of the MT vendor tampering with the output or fine tuning the engine based on the output.
• Word Segmentation and Tokenization must be consistent: If Word Segmentation is required (i.e. for languages such as Chinese, Japanese and Thai) then the same word segmentation tool should be used on the reference translations and all the machine translation outputs. The same tokenization should also be used. Language Studio™ Pro provides a simple means to ensure all tokenization is consistent with its embedded tokenization technology.

Ability to Improve is More Important than Initial Translation Engine Quality

The initial scores of a machine translation engine, while indicative of initial quality, should be viewed as a starting point for rapid improvement which is measured by the test set and BLEU scores. Depending on the volume and quality of data provided to the SMT vendor for training, the quality may be lower or higher. Most often, more important than the initial quality is how quickly the translation engine quality improves. 

Frequently a new translation engine will have gaps in vocabulary and grammatical coverage. Other machine translation vendors’ engines do not improve at all or merely improve very little unless huge volumes of data are added to the initial training data. Most vendors recommend retraining once you have gathered a volume of additional data that is at least 20% of the size of the initial training data that the engine was trained on. Even when this volume of data is added, only a small improvement is achieved. As a result, very few translation engines evolve in quality much further than their initial quality.

In stark contrast, Language Studio™ translation engines are created with millions of sentences of data that Asia Online has prepared in addition to the data that the customer provides. The translation engines improve rapidly with a very small amount of feedback. It is not uncommon to get a 1-2 BLEU score improvement with as little as a few thousand post-edited sentences. Language Studio has a unique 4 step approach that leverages the benefits of Clean Data SMT and manufactures additional learning data by directly analyzing the edits made to the machine translated output.

Consequently, only a small amount of post-edited feedback can improve Language Studio™ translation engine quality quite considerably, and it can do so at speeds much faster and with far less effort than with other machine translation vendors. Asia Online provides complimentary Incremental Improvement Trainings to encourage rapid translation engine quality improvement with every full customization and also offers additional complimentary Incremental Improvement Trainings when word packages are purchased, greatly reducing Total Cost of Ownership (TCO). 

 

An investment in quality at the development stages of a translation engine impacts and reduces the cost of post editing directly, while increasing post editing productivity. While the development of some rules, normalization, glossary and non-translatable term work will assist in the rate of improvement, the fastest and most efficient way to improve Language Studio™ engines is to post edit the translations and feed them back into Language Studio™ for processing. The edits will be analyzed and new training data will be generated, directly addressing the primary cause of most errors. In other words, just post editing as part of a normal project will result in an immediate improvement. Little or no other extra effort is needed. By leveraging the standard post editing process, the effort and cost of improvement as well as the volume of data required in order to improve is greatly reduced. 

Depending on the initial training data provided by the client, a small number of Incremental Improvement Trainings are usually sufficient for most Language Studio™ translation engines to improve to a quality level approaching near-human quality. 

Other machine translation vendors are now also claiming to build systems based on Clean Data SMT. Closer investigation reveals that their definition of “cleaning” is not the same as Asia Online. Removing formatting tags is not cleaning data. Language Studio™ analyzes translation memories and other training data and ensures that only the highest quality in domain data from trusted sources is included in the creation of your custom engine. The result is that improvements are rapid. Even with just a few thousand segments edited, the improvements are notable. When combined with Language Studio™ hybrid rules and an SMT approach to machine translation the quality of the translation output can increase by as much as 10, 20 or even 30 BLEU points between versions.

Comparing Translation Engines – Translation Quality Improvement Assessment

• Comparing Versions: When comparing improvements between versions of a translation engine from a single vendor, it is possible to work with just one test set, but the vendor must ensure that the test set remains “blind” and that the scores are not biased towards the test set. Only then can a meaningful representation of quality improvement be achieved.
• Comparing Machine Translation Vendors: When comparing translation engine output from different vendors, a second “blind” test set is often needed to measure improvement. While you can use the first test set, it is often difficult to ensure that the vendor did not adapt its system to better suit and be biased towards the test set and in doing so delivering an artificially high score. It is also possible for the test set data to be added to engines training data which will also bias the score.

As a general rule, if you cannot be 100% certain that the vendor has not included the first test set data or adapted the engine to suit the test set, then a second “blind” test set is required. When a second test set is used, a measurement should be taken from the original translation engine and compared to the improved translation engine to give a meaningful result that can be trusted and relied upon.

Bringing It All Together

The table below shows a real world example of a version 1 translation engine from Asia Online and an improved version after feedback. Additional rules were added to the translation to meet specific client requirements, which resulted in considerable improvement in translation quality. This is part of Asia Online’s standard customization process. Language Studio™ puts a very high level of control in the customer’s hands where rules, runtime glossaries, non-translatable terms and other customization features ensure the quality of the output is as close to human quality and requires the least amount of editing possible. 

BLEU Score Comparisons Case Sensitive	Asia Online
	V1 SMT	V2 SMT	V2 SMT + Rules	Google	Bing	Systran
Reference 1	36.05	45.96	56.59	30.58	29.64	21.01
Reference 2	35.80	39.31	48.85	32.05	29.94	22.56
Reference 3	38.65	52.31	65.03	35.51	33.17	24.68
Combined References	50.45	66.52	80.48	44.58	41.65	30.26
Case Insensitive
Reference 1	41.30	52.65	59.25	32.18	31.49	22.49
Reference 2	41.01	45.32	51.24	33.67	31.64	23.88
Reference 3	43.99	58.97	67.49	37.15	35.01	25.92
Combined References	56.83	74.35	82.89	46.26	43.68	31.68

*Language Pair: English into French.     Domain: Information Technology.           

It can be seen clearly from the scores above that when all three human reference translations are combined the BLEU score is significantly higher and that the BLEU scores vary considerably between each of the human reference translations. The impact of the improvement and the application of client specific rules can also be seen, raising the case sensitive BLEU score from 50.45 to 80.48 (an increase of 30.03 in just one improvement iteration). One interesting side effect of having multiple human references is that it is often possible to judge the quality of the human reference also. In the example above, the machine translation output is much closer to human reference 3, indicating a higher quality reference. The client later confirmed that the editor who prepared the reference was a senior editor and more skilled than the other 2 editors who prepared human reference 1 and 2. 

A BLEU score, as with other translation metrics, is just a meaningless number unless it is established in a controlled environment. Asking “What is your BLEU score?” could result in any one of the above scores being given. When controls are applied, translation metrics can be used both to measure improvements in a translation engine and compare translation engines from different vendors. However, while automated metrics are useful, the ultimate measurement is still a human assessment. Language Studio™ Pro also provides tools to assist in delivering balanced, repeatable and meaningful metrics for human quality assessment.

Review: Most Popular Blog Posts from 2011

“Blogs are about sharing with authenticity. A good blog can help you really connect deeply with your audience in a meaningful way because the content is not only relevant but insightful and personal. I think most enterprises miss that point. When you do it right, your customers will walk away not only having learned something new but will also feel much more connected to your brand.”     David Armano EVP, Global Innovation & Integration at Edelman Digital

Don’t say anything online that you wouldn’t want plastered on a billboard with your face on it. -- Erin Bury

 One of the things that I enjoy about blogging is the feedback that one gets, and the continuing and evolving  discussion that sometimes comes forth from these posts. I find it helps to clarify my thinking on what really matters, and the critical feedback one gets, on assumptions that may previously go unquestioned is very useful in just evolving my own thinking on these issues. The feedback and the rankings helps me, and others too, I think, to understand what strikes a chord in the reader community, and can also sometimes help to guide further evolutionary thinking on the subjects at hand. This is is a ranking of the most popular (Unique Visitors and Page Views) posts of the year based on the data provided by Google Analytics.

1. Analysis of the Shutdown Announcements of the Google Translate API and the subsequent posts on what this may mean for the translation industry were by far the most popular posts of the year. The original post authored by Dion Wiggins was also referenced by the Atlantic and  other mainstream media and still continues to be an influential view on the announcement today, probably much more so than any other publicly offered opinion in the professional translation industry.

2. The Continuing Saga & Evolution of Machine Translation was coverage of the IMTT 7th Conference in Cordoba triggered active debates and discussions MT, automation and translator compensation in several forums and clearly struck a chord for many.

3. The Future of Translation Memory (TM) is a posting that continues to receive high new visit rates long after it was originally published.

4. The Building Momentum for Post-Edited Machine Translation (PEMT) a number of case studies on the increasing use of post-edited MT to meet business timeliness and production cost requirements.

5. Has Google Translate Reached the Limits of its Ongoing Improvement? More evidence that more data Is not always better especially for MT, but even for Search, and the many reasons to consider the data quality, yet again.

6. The Growing Interest & Concern About the Future of Professional Translation About reactions to the changes underway in translation

7. Standards: the Importance of Measurement A guest post by Valeria Cannavina on how standards can drive quality improvements

8. The Moses Madness and Dead Flowers A post that questions some of the assumptions made by “instant Moses” advocates and challenges the long-term value of these experiments. Strong opinions voiced in the comments.

9. Translation Crowdsourcing An exploration of the driving forces underlying successful translation crowdsourcing efforts.

10. An Exploration of Post-Editing MT – Part I Discussion on the nature and compensation of post-editing MT work.

Please Repeat: Influence is NOT Popularity!  --  Brian Solis

While reader traffic is one way to measure the impact of articles, there are also other ways that capture the relative influence of individual posts. PostRank is one such measure that I think monitors how others reference the posts, and monitors where and when content generates meaningful interactions across the web. They provide a truer picture of the relative influence and impact of individual blog posts, and thus I include the latest PostRank snapshot here. (You can link to the posts through the table on the right of this blog text).  This table shows that some articles that may not have had high direct readership may actually be much more useful to readers and it is interesting to see how different the two lists are though it is clear that the analysis of the Google Translate API shutdown/pay-wall was a major hit no matter how you look at it. 



It is also interesting to note that some older posts continue to strike a chord with readers and remain active in terms of visibility because the themes are longer lived and also perhaps because they ring true. The original post on standards and some of the posts discussing disintermediation were also posts that generate continuing interest and continue to show up in both the Google Analytics and PostRank ratings.

I have noticed that we are getting more clarity on post-editing MT work in many different ways including new models for more equitable compensation. I am hoping to highlight best practices in this area in the coming year as I believe it will be critical to ongoing adoption and success with MT technology. I also think there will be much more to share on best practices of post-editing MT and I expect that we may find that it is not quite the dreaded beast it has often been portrayed to be.

Social Media is not just a set of new channels for marketing messages. It’s an opportunity for organizations to align with the marketplace and start delivering on behalf of customers  -- Valeria Maltoni, conversationagent.com

I would also like to invite some of you to contribute to the discussion in this blog (guest posts) and assure you that I believe in open discourse and think it is useful for many different viewpoints to be aired to get closer to the “truth”. So please don’t hesitate to send me contributions that you think might be interesting to the audience that has been following this blog. I thank you for your support and I hope that the content here will continue to earn your interest and comments to extend the discussion beyond my thoughts on key issues.

For those who are not aware, there are some very interesting videos from presentations at TAUS that I reported on in the 4th ranked posting above on PEMT momentum.   

Videos of presentations and panels at the recent TAUS User Conference in Santa Clara are now available on YouTube for everyone. The links below will take you to playlists on specific themes: 

• The multilingual web – “beyond the translate button”
• Open Translation Platforms
• TAUS members share Moses-related tools, making others’ lives easier
• The Great Interoperability Debate
• Machine Translation Use Cases
• R/time Multilingual Chat and Spoken Translation
• Researchers on Future Translation Technologies
• Collaborative Translation and Innovating Pricing

I wish you all a wonderful holiday season and look forward to sharing observations in the coming year, a year that many say will be a turning point across many dimensions.