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 A computer journal for translation professionals


Issue 17-11-281
(the two hundred eighty first edition)  

Contents

1. The Multiple Birth of Adaptive Neural MT

2. Twicks

3. The Tech-Savvy Interpreter: Interpreting and the Computer? Translating and the Computer 39

4. This 'n' That

The Last Word on the Tool Box

Let's Talk!

In the last Tool Box Journal I wrote a long spiel about a "beautiful wiki site" that "provided a way to articulate and develop requests for desirable technological developments and then send those to all current (and future!) developers." And then I had the gall to say that it's not available yet. Well, now it is. Join me in welcoming . . .

Language Technology Wiki

I think this might be the start to something really special, a place where we as a large and very diverse community can come together, suggest ideas for how to improve translation and interpreting technology, and see those realized in the tools of tomorrow. What kind of ideas? As you explore the site, you'll find the following (preliminary) list under Topics: audiovisual translation, authoring and translation, CAT project management, collaboration, impact of language technologies on the translation process and skills, Interpreting Delivery Platforms (IDP), Interpreting Management Systems (IMSes), machine interpreting/speech-to-speech translation, machine translation, quality, terminology management, Translation Management Systems, translation memory, usability / accessibility / learnability, voice recognition, and web resources. Don't tell me that you (especially as a Tool Box Journal reader) can't think of anything to say about any of these topics. Of course you can! (And as you do it, consider this (under Rules): "Do not criticize or denigrate companies or tools and likewise do not specifically advocate or support them. This is a community discussion platform inclusive of users and makers of translation technologies, but it is not a place to take out grievances or advertise. Naturally it is fine to identify tools in discussions to compare how technology should evolve.")

I want to give a big and heartfelt thanks to Iulianna van der Lek-Ciudin, Tom Alwood, Alexander Drechsel, Marie-Sophie Petit, Martin Kappus, and Barry Slaughter Olsen who made this possible. Be sure to connect with them on LinkedIn and say thank you. 

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1. The Multiple Birth of Adaptive Neural MT

After the rush to embrace neural machine translation (NMT), it has finally arrived at our -- the translators' -- doorstep with adaptive models. And at this point, not just one has been released but three, two just in these past couple of weeks. They really are three different kinds of models because they each use a very different approach to adapting the MT output -- so different, in fact, that I wonder whether they should all be called by the same "adaptive" moniker.

Let's start with the first one, KantanMT's neural system, which has been in use since March of this year.

As I talked with KantanMT's CEO Tony O'Dowd about this, it became increasingly clear to me how disadvantaged an MT provider is who does not also offer a full-fledged translation environment. To put it differently: There are equally important fields of responsibility on the side of the translation environment provider and machine translation provider to make machine translation work smoothly and productively in a translator's workflow.

Let's back up first, though. Here's how KantanMT deals with neural machine translation. As for any neural machine translation system, KantanMT's initial processing requirements to train neural engines are very high. Tony mentioned that for the first training pass, the processing of a 100-million-word bilingual corpus takes three to four days. It's overwhelming to imagine that this would have to be done again and again for an adaptive system. The nice thing about neural machine translation, however, is that the ongoing training for adapting the translation model does not require a complete retraining (as it did in many cases with statistical machine translation, with the notable exception of Lilt -- see below).

KantanMT has set up the incremental training of the neural MT engines by collecting data in a TM of sorts (which first is just kept in the temporary cache of the server but is converted into a file once the user logs out). At certain intervals (but not every time a segment is finalized), this TM is fed into the neural engine for adaptive purposes. In the meantime (and after the passing of the data), the TM (in KantanMT-speak "Total Recall") is used as a translation memory that acts very much like most TMs in combination with MT: perfect matches and any match with a fuzzy match rate of 85% or higher is preferred to MT suggestions and entered automatically to be confirmed or edited by the translator. Otherwise, the neural machine translation engine suggests translations. Since the engine in most cases is solely trained on the data that the client has provided and is being adapted and improved as the projects are being processed, there is a relatively good chance that the suggestions are of reasonably good quality (at least as far as terminology is concerned).

You might have the same question I had: How does this process using a front-end TM differ from any other translation environment tool? It doesn't really, if not for the fact that both the MT and the TM are cloud-based and therefore available to everyone in a real-time translation team. And while this is also available in other server-based workflows offered by the various translation environment tools, more often than not, this is not set up for projects that you and I work on.

But here is the hitch: While many translation environment tools support the use of KantanMT, only a handful allow its use upstream (i.e., sending data back to the KantanMT's cloud so it can then be used across translators). The ones that do include Memsource, Trados Studio, and (in a limited fashion) Across. Tony's (unsurprising) prediction for 2018? "Next year adaptive MT will become the big thing that will be seen in CAT tools," meaning that tools like KantanMT will have more complete access.

The main clientele for KantanMT is clearly the translation buyer. Early on there were plans to also offer products to other stakeholders, but its easy access to large amounts of focused data made the translation buyer the primary target. Still, additional features that would be helpful for translators would be welcomed, such as more interactivity between the machine translation engine and the "Total Recall" TM engine to "fix" TM matches (with MT) or MT suggestions (with TM).

 

The other engine that has just gone live (within Translated's translation environment tool MateCat and the paid Pro edition of the MyMemory app for SDL Trados Studio) is ModernMT. ModernMT is a three-year EU-funded project with a number of partners, including Translated, much like MateCat itself was a few years ago. If you remember, MateCat's original purpose was to "investigate the integration of MT into the CAT workflow [with] self-tuning MT that adapts MT to specific domains or translation projects and user-adaptive MT that quickly adapts from user corrections and feedback" (Source: Proceedings of the 17th Annual Conference of the European Association for Machine Translation). While the adaptive system worked reasonably well, that part was unceremoniously and frustratingly dropped from MateCat, and the EU agreed to confer another three-year contract. This time the adaptive MT is here to stay, according to Translated's Alessandro Cattelan, whom I spoke to for this report.

You gotta feel for all these MT developers working so hard on the cutting edge of technology when, just like that, a new and improved technology enters the field and, alas, all their prior work is ready for the trash heaps of technology history. I'm not completely sure this is entirely how it went with ModernMT, but I would guess it's pretty close, since only this summer they turned their attention to neural machine translation after having spent more than two years on statistical, adaptive MT. Amazingly enough, they were still able to present a result just a few months later.

The adaptive part of the technology is fundamentally different than other adaptive engines because there are actually no changes in the baseline engine happening at any time. Instead, the system uses a technology called "instance-based adaptive NMT." Similar to KantanMT but for a different purpose, this consists of the translation request first being sent to a TM layer (which can consist even of a relatively small TM as long as it's highly-tuned). With similar segments found in that TM layer, the NMT engine's "hyperparameters" are adapted on-the-fly so that a more suitable suggestion is generated. This concept is based on this paper by the Fondazio Bruno Kessler, which is part of the consortium working on ModernMT.

The benefit -- in theory -- is that you don't ever need to actually train a specific MT engine, but you can instead use a large generic engine whose suggestions are specialized by having the query parameters adapted as the translation is happening.

I tried to work with the engine in MateCat after I talked with Alessandro, but I wasn't able to get any suggestions other than the ones from Google Translate (you can't actually choose the engine because the system selects it for you).

I have to be honest and say that I don't completely understand the concept of this technology, but if it is indeed able to produce better results, then all power to it. I am pretty sure, though, that "adaptive" is a bit of a misnomer. Not because the machine translation suggestion is not being adapted (it is!), but because the process is different from what is typically understood as adaptive MT. Maybe a term like "responsive," "reactive," or even "ad-hoc-tuned" might be better.

ModernMT is completely open-source (in fact, you can download everything from its website), but that does not include the data, of course. This is where Translated's hyper-TM MyMemory comes in and where the company sees a possibility for itself to market this solution -- to translators and LSPs as a paid service through MateCat, Trados, and possible other translation environments and to large translation buyers as an in-house solution. (According to Alessandro, at least one large Silicon Valley company has already done some extensive testing and found the results better than those from Microsoft Translator Hub, the trainable machine translation engine Microsoft offers.)

I also asked Alessandro how the non-adapted baseline engine compares with the Google Translate NMT engine, and he was honest enough to say that it produces worse results -- unless the adaptive process is taking place and then it's better.

(continued below)

 

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1. The Multiple Birth of Adaptive Neural MT (continued)

Lilt has also released a neural MT engine (for now only between English and German and English and Chinese and only for select users -- see more on this below). Since the team at Lilt consists of remarkably young and energetic folks, they not only introduced their new engine but also completely converted the user interface, oh, and rebranded (see below as well).

And, yes, they also settled the silly lawsuit SDL had brought against them (see edition 273 of the Tool Box Journal). (The very non-communicative and, it seems, legally prescribed official statement from Lilt: "Lilt is pleased with the resolution of this dispute. The settlement was mutually agreeable. We will continue to focus on products and services that democratize access to information.")

Anyway, I was really intrigued and -- I admit it -- at first puzzled about Lilt's move toward neural machine translation. Here's why: Lilt essentially broke down all the borders that existed between the different assets translators use by creating one single database for all the data used by the (at that point, statistical) machine translation, the translation memory engine, and the automated term extraction/termbase engine. This was made possible because the data was sitting in an open text-based table that could be utilized for any of these options. Even though I'm not super-technical, I also knew that wasn't possible when it came to neural machine translation, where the translation model really consists of numbers rather than language data. So my confusion had to do with me wondering whether Lilt had thrown all of its basic concepts overboard.

Turns out it hasn't, at least not completely. As in the previous incarnation, there is still a massive text-based table containing data that the TM and the termbase features are based on and that is being used to train the neural machine translation engine initially and continually. (The difference involves a different level of immediacy of the data in the data table and how it's used for machine translation purposes.)

Lilt's NMT engine is trained on the same bilingual data as the previous SMT engine (that's at least true for the four existing language combinations) minus the monolingual data needed for SMT but not for NMT.

The engine interacts with the user as he or she translates through the same process as previously: Every time a word is entered, the machine translation engine calculates a new suggestion for the rest of the segment to match the entered data. (Lilt's Carmen Heger, whom I talked to about this, mentioned a barely noticeable increased delay in the response. She was right, it's hardly noticeable; in fact, I didn't notice it.)

Also like before, every time you confirm the translation of a segment, the machine translation data model (which now runs parallel to the data table) is immediately adapted -- in fact, much more efficiently than before. And just as in the case of KantanMT, while the initial training and so-called batch updates (like when you upload a whole translation memory) are very resource- and processing-intensive, incrementally adding to the neural engine is not, allowing it to be done on regular CPU servers (vs. the more powerful GPU servers).

So, what about the quality of the suggestions? One important consideration when answering this question is that with Lilt it does not primarily have to be the initial suggestion for the whole segment to be useful as long as the constantly evolving suggestions for each segment are useful. In my very limited and unscientific testing in EN>DE, the initial suggestions seemed OK but tended to be less accurate than Google Translate or DeepL (for test purposes I usually copied and pasted the same segment in the other interfaces). But when I diverged from the original suggestion they became ever more helpful, and tended to be more helpful than in the earlier SMT-based Lilt.

(Here is a bit more scientific data: Lilt ran a case study with one of their corporate partners comparing the statistical vs. neural quality by professional translators. The results: NMT was chosen 44% of the time, 29% of the time translators chose SMT, and in 27% of the time it was about equal.)

Since we're talking about corporate clients, you might have seen that Lilt has also done some rebranding with the slogan, "The New Engine for Enterprise Translation Workflows." What does this mean for translation professionals? Not much as far as the availability of the tool, but it does reveal that we haven't been as eager to switch to a new technology paradigm as some might have expected. And that in turn may point to something more profound (forgive the over-generalization): Traditionally translation professionals have been very slow to accept technology. But once they accepted translation technology as a normal part of their work environment, they were (and are) just as slow to change their chosen technology (which was so hard to accept in the first place). Maybe it's just the human condition, but I think what we might not completely understand is that being technologically adept by definition means a constant willingness to change the technology that becomes available as it develops.

Now, I'm not saying this because I think Lilt is the one and only kind of technology out there. It isn't. There are plenty of interesting new tools and technologies. And if you honestly look at yourself, are you completely open to continuously moving on? (I have to admit that I'm not, and I also know that I would be a better translator if I were.)

To finish this up, as mentioned above, Lilt also has a complete new interface as you can read in this document. I like that there's no separate editing interface, it's now very easy to switch between a vertical and a horizontal display, and there is a better sense of accessibility -- even as far as accessing available options and keyboard shortcuts.

I did not like the "on-demand tag editor" which, while a good idea in general, did not work as seamlessly as it should have.

Oh, and I forgot to mention that the neural mode is at this point available only for corporate clients (and LSP clients on demand), but it will be available for all sometime in the new year with all language combinations enabled. 

 

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2. Twicks

Aside from LinkedIn, Twitter is my preferred social network, and it can also be very productive -- productive for networking with colleagues, for meeting clients, and for publicly displaying who you are and what you stand for. In relation to other forms of social media, it's also less unproductive because it doesn't necessarily require you to read never-ending posts (though it might lead you to some of those), and you're supposed to express yourself in a relatively precise manner (though this brevity has been a little diluted by the recent extension to 280 bytes, i.e., 280 single-byte and 140 double-byte characters per tweet).

In looking at some colleagues' tweets, I've noticed a couple of tips that might be worth repeating:

  • Never start a tweet that is supposed to be seen by everyone with @username because this will be displayed on Twitter's homepage only for username plus everyone who follows username and you. Enter a period (or some other character) before the @ sign. The same is true if you reply to someone.
  • If you feel like tweeting about private things as well as professional things, set up two different accounts. Really! I immediately unfollow other tweeters who start to regularly report on personal matters -- and while it's not important what I do, many of those who you really want to reach are doing the same. It's not enough to say that you don't promote your Twitter account anywhere in your professional materials -- as soon as you participate in a professional discussion, you are effectively promoting it.
  • If you get frustrated with someone's barrage of tweets but you don't want to frustrate them by unfollowing them, you can also "mute" that account by clicking the down-arrow at the top of one of their tweets and selecting Mute @username.
  • Give credit where it's due. If you find something through someone else's tweet or email, mention that in any related follow-up tweet you might send yourself. If you post a link to an article that someone interesting wrote, research his or her Twitter handle and mention that in the tweet as well. And if you're retweeting someone but have changed some of the content, precede it not with RT (retweet) but with MT (modified tweet) or HT (hat tip).
  • #Use #hashtags judiciously, not to #the point that #your #tweets are really #hard to #read. It's probably a good idea to set a hashtag in front of a central term (like #translation or #xl8), but I even prefer not to do that. Instead, I really enjoy using the hashtag as a way to explain or comment on my own tweet. #NewAreaofLinguistics

Here are some other helpful tips:

  • It's a good idea to download your archives of tweets every once in a while -- especially if you tweet a lot. This will help you to quickly locate a tweet you might have sent a long time ago. You can do that under Settings> Account> Request your archive.
  • You can also use helpful keyboard shortcuts in the non-mobile Twitter interface. To see all of them listed at once, just press the question mark key when you're in the Twitter web interface. (Cool, huh?)
  • Obviously the best way to (legitimately) gain more followers is to post interesting and engaging tweets. If you want to speed it up a little bit, here are a couple of ways to follow others so that you engage with them and hope that they will follow you back:
    • When there are translation-related conferences happening (and they're always happening somewhere...), click on the conference hashtag to see who is tweeting from the conference. Not only will you get a good (and cheap) overview of what's happening at the conference, you can follow the tweeters and maybe even engage them in a conversation while staying under the hashtag umbrella.
    • Search tweets under translation-related hashtags such as #xl8 and consider following those.
  • While it's not possible to format text in tweets the conventional way, you can perform a Unicode conversion. This converts your text into formatted look-alike characters. The drawback is that this text is not searchable.
  • Also not searchable but helpful for text that is too long to tweet: make a screenshot of text and embed it as a graphic in a tweet.
  • An additional benefit of an embedded graphic is that, unlike hyperlinks, it doesn't count against the character limit. Plus you can tag up to 10 Twitter users in a graphic to involve them in a discussion.
  • If your tweet links to a source in a language other than the one you usually tweet in, be sure to note it [SV] or [ZH]...
  • Don't use services like "The Daily ... is Out" (paper.li) or scoop.it. This is annoying for followers since it requires more clicks. Also, don't have services like commun.it or SumAll.com tweet how many retweets you had and how many followers you gained last week. No one is really interested!
  • Don't auto-tweet from LinkedIn, Xing, or Facebook and have part of that tweet truncated.
  • When quoting from an article or blog post, be sure to <offset> your own comments.
  • When tweeting a link to a news article, research the Twitter handle of the article's reporter to include it. Do that especially when you're critical of the article. This can lead to great discussions that might help you and others within the world of translation.
  • When tweeting a link to a news article, consider making the headline in the tweet descriptive rather than using the original title, which is often formulated as click-bait.
  • To do an advanced search in Twitter, you can go to twitter.com/search-advanced. Or you can use the Twitter-specific syntax in Twitter's regular Search field. Helpful search parameters include from:user and to:user to search for sender and recipient (if applicable) respectively.
  • If you want to shorten a URL in a tweet, use xl8.link as a URL shortener to show some pride in being a translation professional.

Finally, don't just gain colleagues as followers but also potential clients. Think of Twitter as your interactive, ever-evolving business card. You'll be glad you did. 

 

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3. The Tech-Savvy Interpreter: Interpreting and the Computer? Translating and the Computer 39   (Column by Barry Slaughter Olsen)

I recently returned from London where I attended the 39th installment of Translating and the Computer (TC39), a yearly scientific conference organized by the International Association for Advancement in Language Technology, or ASLING for short. Translating and the Computer has been around since 1978 and will celebrate its 40th anniversary in 2018. The original organizers were prescient indeed and clearly foresaw the potential of the computer to assist with translation.

The conference brings together researchers, academics, and translators (and this year interpreters too) for two days of learning and discussion about the latest developments in machine translation, corpus linguistics, translation memories, and other technology topics affecting translation. As one might expect, the big focus this year was on neural machine translation (NMT)-how it works and how it might be folded into the translation workflow.

For interpreting and technology, however, TC39 was a watershed. The organizers decided to include a track on interpreting and the computer for the first time in the history of the conference. They even dedicated the Friday keynote address to interpreting and the computer-a speech by Dr. Alexander Waibel on the current state of speech-to-speech translation and the use of deep neural networks for speech recognition combined with machine translation. The interpreting track ended with a panel discussion on "New Frontiers in Interpreting Technology" moderated by Professor Danielle D'Hayer from London Metropolitan University. Panelists included Dr. Anja Rütten, Alexander Drechsel, Joshua Goldsmith, Marcin Feder and yours truly.

I could go on and on about the presentations, from what kinds of tablets are being used by professional interpreters in the field (Joshua Goldsmith), to an overview of terminology management tools for conference interpreters (Anja Rütten), to automatic speech recognition (ASR) and how it is being combined with terminology management software to provide automated terminology lookup in the booth (Claudio Fantinuoli). But I won't. Instead, If you are interested in the topics covered at TC39, I encourage you to read Alexander Drechsel's November 21st blog post reporting on TC39. It's full of detailed information about each of the presentations and even includes a pair of videos we shot live from the conference.

Here's to hoping this initial foray into interpreting territory by ASLING is the beginning of a lasting effort to provide a forum for research and discussion not only on translating, but also interpreting, and the computer. If you interested, mark your calendars now. The 40th anniversary edition of Translating and the Computer (TC40) will take place on November 15-16, 2018 in London.

Do you have a question about a specific technology? Or would you like to learn more about a specific interpreting platform, interpreter console or supporting technology? Send us an email at inquiry@interpretamerica.com

 

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4. This 'n' That

A book that is most certainly going to be helpful for novices to the world of translation as well as to the majority of more experienced colleagues is Language of Localization, edited by Kit Brown-Hoekstra. It gives a great, two-page-each overview of 52 topics (by 52 different authors) that are relevant to the modern translation and localization workflow and technologies.

 

You might remember that I strongly encouraged tool vendors awhile back to offer switchable access to either the statistical machine translation or the neural machine translation engine of Google Translate. A number of them obliged, with the latest being Okapi. (If you don't know much about Okapi, and especially its flagship tool Rainbow, there's going to be an excellent article about it in the next ATA Chronicle. You don't even have to be an ATA member to read it: Its online edition is available right here. While you're there you can also read the interesting current article about OmegaT.)

 

Another tool that had this option much earlier than most and should have been mentioned by me is Smartcat, the translation environment tool that was originally developed by ABBYY and is now independent. The way Smartcat handles machine translation is a little different than other tools. In other tools -- and depending on which machine translation engine you use -- you either have to enter your API key or information about the server and the respective authorization to access it. Depending on your use, you then have to pay the machine translation provider. Smartcat, on the other hand, acts as a reseller between you and the MT provider, so you pay a usage fee to Smartcat unless, and this is important to realize, you agree to send your edited data back to Google, Microsoft, or Yandex (the three providers that are supported). Clearly this is not a good option for many translators, so you have to really understand which option you choose and what that means if you are using Smartcat.

Another option in Smartcat starting next month will be Intento, a relatively new Russian company that is primarily geared toward larger translation buyers but will make its foray into our world starting with Smartcat (other translation environments will follow soon). Intento is somewhat similar to Fair Trade Translation (see in issues 254 and 263 of the Tool Box Journal). It's a tool that allows you (better: will allow you) to get an automated estimate of which machine translation engine suits your present project best and at what cost. The engines include engines by SAP, SDL, PROMT, IBM, DeepL, Microsoft, Baidu, GTCOM Yeecloud, Google, Yandex, and SYSTRAN. It remains to be seen whether this is usable from a translator's perspective, especially when it comes to issues like confidentiality. (I'll look into that more deeply once there are more connectors to other tools.) Still, it was very interesting to talk with Kontantin Savenkov, Intento's CEO, earlier this week. Here is one insight that he shared. As long as English is either the source or target language, there is a relatively great likelihood that Google Translate (or DeepL for the languages that it covers) will come out on top by Intento's automated assessment. As long as that is not the case, all bets are off. Why? Because the system then uses English as the "pivot language," so there are two machine translation processes taking place (in, say, Chinese to Indonesian it would be Chinese to English and then English to Indonesian).

Again, more on that once it's more widely available.

 

Helen Eby recently did an interesting survey on the use of monitors among translators that some of you will find it interesting. You can find it right here.

 

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The Last Word on the Tool Box Journal

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