You don't have your own memoQ translator pro license yet? memoQ offers 30% off on all new translator pro licenses for Tool Box Journal subscribers!

Read our guide on the purchase process and apply code CAM_TBJ202030_PS in the memoQ webshop. Offer expires at midnight October 21 in any time zone.

Emma is one of the most knowledgeable users of Trados Studio and loves to write about it on her popular blog. I asked her whether she would like to write about the latest version for the Tool Box Journal, and I think you will really enjoy what she's provided.)

As a member of the SDL Trados Studio beta team, I'm in the privileged position of being among the first to get my eyes on new Studio versions. Beta testers help the developers identify bugs and reproduce them, we download one build after another and double check that issues are solved and that new changes – both minor and major – work as intended. As a result, by the end of a beta testing cycle, the product is more robust, and we've gained an in-depth understanding of all the new features. I'm happy Jost asked me to share some of those insights with you today.

SDL Trados Studio 2021, released in true SDL style in the year preceding its name, follows the now common practice of bringing the best AppStore plug-ins into the core product. The AppStore is a brilliant way for users to customise their Studio experience and pick and choose apps that enhance their workflow or productivity. But many users aren't aware of this fantastic repository or don't keep track of new additions. Studio 2021 makes apps easy to discover by bringing the whole AppStore into Studio.

The pop-up SDL AppStore in Studio means you can browse through the apps, batch download any that catch your attention, and give them a go. Love an app? Keep it! Not what you wanted? Uninstall it in just one click. A smart update feature in the AppStore-in-Studio will gently nudge you when apps need updating. No pesky notifications -- just a quick and easy way to stay up-to-date. If you're wondering which apps I've installed out of the 200 plus in the store, take a peek at the screenshot above.

Another new in-the-box app is SDL Community Inside. Support for Studio users has improved in leaps and bounds over the past decade, and now you can get help fast by clicking a forum link inside Studio.

The Advanced Display Filter first appeared in Studio 2017 and has been expanded in Studio 2021 to include Boolean and/or searches; filtering by text inside tags; specific colours; fuzzy match ranges; back references in regular expressions; and more. A reverse button displays the opposite of any applied filter.

Auto-localisation now goes beyond the basics of auto-substituting source language dates, measurements, and numbers with target language equivalents. Translation memory settings can now be tweaked to recognise poorly formatted source dates; specify whether a currency symbol goes before or after the amount; and which thousand and decimal separators to use specifically for numbers and currencies.

In a hurry? Studio 2021 now has faster segment merging, term adding, file filtering, project setting editing, and shortcut flexing.

Finally, owners of Studio 2021 Freelance editions will be delighted the five-language limitation has been lifted (although individual projects are limited to three target languages). More importantly for some, the Freelance edition now allows you to create AutoSuggest dictionaries (ASDs) for free. This functionality, which autocompletes what you're typing based on TM content, was bundled with Studio 2009 but became a paid extra in Studio 2011-2019. I'm one of those diehards who still likes to use a solid, domain-specific ASD combined with newer technology such as on-the-fly suggestions from fragment recall and match repair.

I'm well aware you might be skim-reading this review to home in on this part of SDL Trados Live. After all, the pre-release hype focused almost exclusively on this exciting "next-generation" functionality, promising "flexibility to work how, when and where you like." And SDL delivered.

SDL Trados Live is a platform for managing, translating, and revising projects online that syncs with Studio 2021 desktop. Integration between desktop and cloud means you can set up a project in Studio or online, start translating, and then switch to the other platform to finish the translation or revise it. You can even manage the project in a dedicated phone app.

The Online Editor has an SDL-green-themed, roomy, intuitive interface. A solid set of features includes:

Syncing between desktop and cloud works well. Once you've set up a project, you really can stop translating mid-sentence in Studio and pick up where you left off in the browser of any computer you lay your hands on.

SDL Trados Live is indeed more flexible. Translating and working in a browser means that Mac and Linux users can finally access Trados without Parallels or similar workarounds. And the capability of uploading desktop resources to your personal cloud space brings hybrid working to a new level while respecting confidentiality and ensuring security.

My take on SDL Trados Live? Well, the product is still quite green (not referring to the theme colour this time). Studio desktop is more feature-rich, although I'm sure many features (such as Studio package capability, AppStore access, file merging, and replace all) will make their way into the Online Editor in the coming months.

I'm a bit concerned about always needing to be connected. Many freelance translators work from home, some in rural or poorly connected places. Sluggish segment look-up and dropped connections may hamper full engagement with this new hybrid way of working. Ad hoc switching from an exclusively local project to the cloud is impractical because of lack of access to local translation memories. It's an all-or-nothing choice.

Finally, although the Online Editor is simple to pick up and use, project set-up and workflow has a steep learning curve. Do watch this short video before you dive in.

Note that Studio 2021 license holders are being given 12 months free access to SDL Trados Live Essential, an edition for individual use only. The next edition up is SDL Trados Live Team, which is geared towards project managers and translators who want to share resources while translating in Studio or the cloud.

Subscription licensing opens a Pandora's box. Until now SDL Trados Studio has been sold exclusively through perpetual licenses. But this time next year, at the end of our free SDL Trados Live access period, we'll have to decide whether we want to pay an additional €75/year for continued access, keep our perpetual license only, or -- maybe when the next Studio edition is released -- move to a subscription model for access to Studio and the cloud for €295/year.

SaaS (software as a service) is an increasingly popular licensing model (in the translation world we already have MS Office, IntelliWebSearch, PerfectIt, and Xbench, to name a few), so it's no surprise that SDL is moving in that direction. Let's just hope that both licensing models (perpetual and subscription-based) will continue to be offered in the long term. That will keep all current users happy and tempt more translators to test the waters of hybrid working.

Emma Goldsmith is a freelance Spanish-to-English medical translator based in Madrid, Spain, who made a career switch from nursing some 25 years ago. She writes a blog called Signs & Symptoms of Translation about medical translation and SDL Trados Studio, and currently serves as membership chair on the governing council of MET (Mediterranean Editors and Translators).

Your style, your wording, your content. Utilize translations quickly and reliably

throughout your company. Machine translation powered by STAR MT.

Watch the short video for more information on STAR MT functionality and usage:

In last month’s column, we shared the first part of our discussion with Barry S. Olsen on the history of interpreting technology.

You can also check out the full video interview on our YouTube channel. 

In a seminal article, you looked at the three T’s: technology for delivering interpreting services, technology that augments interpreter performance, and technology that might replace us altogether. First, let's focus on how the technology that augments interpreter performance has changed over the years.

Specific technologies are only now beginning to emerge because of artificial intelligence. Some platforms are using AI for terminology extraction, quickly aligning documents, or practicing vocabulary. Anything that can streamline interpreter preparation will increase our performance.

The greatest opportunity is with AI and speech recognition. Speech recognition is improving significantly, and the data for different dialects and accents is getting better and better.

As computing power increases, speech recognition will improve, and include recognition of proper names and numbers. Claudio Fantinuoli has been experimenting with this for some time at Germersheim. We've seen some pretty amazing videos.

Baidu is actually replicating -- to the extent possible -- the ability to adjust décalage. They're trying to figure out how to have the program wait three, five, or seven words before deciding on the translation. Human interpreters do that automatically all the time.

When I was studying at the Middlebury Institute of International Studies in the mid ‘90s, you would hope to get some kind of document to prepare for assignments. Video on the internet? YouTube wasn't even around yet! You did what you could with some photocopies. Now, my students research the person they’ll interpret, find a keynote address, listen to snippets of their motivational speech and jokes and get used to their accent and speaking style. That was unheard of just 20 years ago!

But because of the virtualization of everything -- software eating the world -- the Interpreters' Helps and the InterpretBanks of the world exist. We need to support these developers, reach out to them, use these products. That will all come back, be a positive influence on our profession and help us move things forward.

Individuals and the international organizations have adjusted to online work. People are saying, "We're doing this online anyway, why don't we just add languages?" The net effect is going to be more work, but it's going to be different work: streaming video and providing live simultaneous interpretation remotely. People will just select their language of choice. We're going to see both smaller and larger meetings. When people realize the efficiencies of the online format, some meetings will remain online.

What I'm reading in some blogs echoes almost to the letter the comments made about the “telephonists” in the official League of Nations reports. I showed quotes from those reports at a presentation. When I said they were from July 1930, the jaws dropped.

Initially, there was no need for simultaneous and plenty of reasons to avoid it. Interpreters were happy doing consecutive. But during the Nuremberg trials, they needed multiple languages quickly. Consecutive just wouldn't do.

PS. Questions or ideas about interpreting technology? Drop us a line at [email protected]! We do the research, so you don’t have to.

Wondering how speech recognition can help you interpret better? Our on-demand “Speech recognition for interpreters: Numbers and terminology made easy” course will show you the ropes.

Grab 30% off with coupon code TECHSAVVY and this link until 10/14/20.

It has always bothered me that there seems to be a serious underrepresentation of women who are involved in the development of machine translation. Since it didn't make much sense for me, a man, to write and complain about that, I asked three women who are involved in machine translation in academics and development to talk with me about the topic. It ended up being a phenomenal exchange (with me on the receiving rather than the giving side). Without further ado, here it is:

JOST: I'm so glad that the three of you -- Lynne Bowker, Sharon O'Brien and Vassilina Nikoulina -- are willing to talk with me about women in machine translation development and women in the academic pursuit of machine translation.

But first, would you mind introducing yourselves?

LYNNE: I grew up in Canada and I completed my initial BA training in translation.

I'm a certified French-English translator, and I worked briefly for the Government of Canada's Translation Bureau and as a freelancer before returning to university for graduate studies. I completed an MA in Translation (University of Ottawa) in the early 1990s and became interested in technologies for terminologists and translators, which were just beginning to emerge on the market. I continued my studies in Language Engineering and I earned a PhD from the University of Manchester Institute of Science and Technology (UMIST), which is now part of the University of Manchester. My first academic job was at Dublin City University in Ireland, where I taught both translation and computational linguistics. In 2000, I returned to Canada and I'm currently a Full Professor at the University of Ottawa with a cross-appointment between the School of Translation and Interpretation and the School of Information Studies.

JOST: Let me add one more thing to that, Lynne: You were recently chosen as a Fellow of the Royal Society of Canada. Congratulations!

SHARON: I did a BA in Applied Languages (with French and German) at Dublin City University (DCU), which was effectively a translation training programme. In my final year, the lecturers introduced the translation system from ALPNET and I was hooked on translation technology! I had the opportunity to do a Master's by research with the then Eurotra project, where I investigated the effectiveness of MT for 'sublanguage' (or LSP / Language for Special Purposes). Afterwards, I moved to Luxembourg for a three-month internship with the European Parliament, where I had the joy of using the DOS version of the Trados Alignment tool for three whole months (T-Align, the precursor to WinAlign) to align translations in eleven languages of MEP's CVs so the translation memories could be used for the upcoming elections. Somehow, this qualified me as a 'language technology specialist' for a localisation company in Dublin, who were interested in introducing Trados in their workflow. My job was to define the localisation processes with a TM tool and to train and support the translators. At one point, a very adventurous client wanted to test machine translation (still RBMT at the time), so I was responsible for that project. After a few years of working in the localisation sector, I decided the time was right to do a PhD and that MT and post-editing was an interesting topic, so I went back to DCU and completed that in 2006, after which I became a faculty member of the School of Applied Language and Intercultural Studies.

VASSILINA: I grew up in Russia where I completed my initial studies in Applied Mathematics. In 2003 I arrived in France at Ecole Polytechnique where I've completed (the equivalent of) a Master's degree in Computer Science. I first got acquainted with MT during my first internship at Systran in 2005. Despite my mathematical background I've always been interested in foreign languages studies, which is why I decided to pursue research in Machine Translation. I completed a PhD at the University of Grenoble and Xerox Research Center in Europe (XRCE) in 2010. My PhD topic was centered around Statistical Machine translation. After completing my PhD I kept working on MT and other NLP-related topics within Xerox Research in Grenoble. In 2017 Naver acquired XRCE. As a follow-up of this acquisition I had a chance to spend 10 months with the Papago team (Naver Machine Translation Engine) in Korea in 2019. It was a great and challenging experience that allowed me to make a step from the MT research world to its practical application, and at the same time act as an active MT user in my everyday life (I used Papago translator every day, several times a day!). I am now back in France and back to my usual research activities, but I try to keep in touch with Papago colleagues and collaborate on different MT-related subjects. 

JOST: Great. Let's start then: Do you think it's true that there is a difference between the number of women working with MT in academia and industry?

LYNNE: This seems a bit anecdotal. Are there any stats on how many women work with MT in academia/industry vs how many men? I don't know of any stats on this, unfortunately, and I really don't have a good grip on the nature of the gender (im)balance other than in the most vague or general way. Also "working with MT" covers a lot of ground – user vs developer and everything in between.

JOST: Thanks for pointing that out. I just checked with the Group Program Manager for MT at Microsoft and he pointed me to a document that showed that of all the technical roles at MS, approximately 20% are held by women (see here). He confirmed that this is approximately the same for MT development. And it is indeed "MT development" that I had hoped to focus on in our discussion, but if you have insights on different kinds of usage between female and male users, I'd be very interested in that as well.

LYNNE: I work at a School of Translation and Interpretation, where our goal is to educate language professionals, including in the use of translation tools (both CAT and MT). So most of my MT-related attention focuses on the user perspective. I don't work directly in tool development per se, and the students that I work with don't usually go on to work in development either; like me, they are language professionals and language researchers, who have an interest in how translation technology is used by language professionals and others. In my experience, both in Dublin and in Ottawa, the development work is more often being done by researchers in computer science departments. Of course, there is conversation between the translation school and the computer science department, but the development work is principally driven by the computer science researchers. Here in the city of Ottawa, which is Canada's capital city, we also have the National Research Council (NRC), which has a very active R&D team working on language technologies, including machine translation (e.g., the Portage system). Although there are some women researchers in both the university computer science department and at the NRC, they are certainly in the minority. In contrast, women are in the majority at the School of Translation and Interpretation, particularly in the student body. So in my lived experience, women researchers are more often found working on the user side of MT, while the development side is more dominated by men researchers.

VASSILINA: I am on the side of MT development, and the observation here is the same as for Computer Science in general: there are many more men than women in this domain. I am not sure that there is a difference between the industry and academia, though it seems that it is already the case among Computer Science graduates, which are then "equally" (?) distributed between industry and academia, I believe.

SHARON: My experience would be similar to Lynne's. If we are focusing only on MT 'development', then the majority are male, but I see a growing number of female academics in computer science in general and in the field of NLP specifically. However, I think this represents the traditional gender imbalances between science and humanities. If, on the other hand, we broaden out what we mean by ‘working' with MT, then the picture is more positive. I know of many women working in roles such as MT client support, MT evaluation, MT process integration and, of course, MT usage.

JOST: Maybe I'm barking up the wrong tree here, but don't you think that academia (i.e., research) and development work very closely together in the case of MT?

SHARON: Yes, that has been my experience, but I would not say it's true of all researchers' experience. More and more, the research funding agencies are requiring collaboration with commercial and not-for-profit organisations and they are (quite rightly) demanding evidence of "impact" for the "citizens" they represent. We will hopefully see more collaboration as a result. The challenge for those of us in Translation Studies is to ensure that we are not last-minute add-ons to projects that simply tick a box. My message to MT researchers is: the Translation Studies and professional translation community is here, we have a lot to offer and we are open for collaboration. Develop with us not for us.

LYNNE: Yes, academics and developers do work closely in the case of MT, but at the moment, the academics are coming more often from computer science departments than from translation departments. But I agree with Sharon that things are moving in a positive direction. So-called "action" or "participatory" research, which originated in the population and public health domains, is being adopted more widely now and it's very relevant to MT research. In a nutshell, participatory research means that researchers are taking steps to include the communities that their work is intended to help more fully in the research process. As Sharon notes, the idea is that the community -- such as MT users -- would not be an afterthought but would be more active participants in the research design process as well. In this way, the research and resulting tools would hopefully better meet the users' needs.

FREE webinars presented by Studio 2021 expert and translator, Nora Díaz.

For beginner, intermediate and advanced users! REGISTER NOW.

JOST: So, you don't think that machine translation development would benefit from proactively working with linguists as part of their development teams? (I have admittedly asked developers this question a number of times, both in the areas of statistical and neural machine translation, and have typically received strongly worded answers -- which I will not disclose here -- but I would be interested in what you have to say about this.)

VASSILINA: That's an interesting question. I personally have been working with linguists on several occasions during my career (mostly on NLP problems other than Machine Translation) and it has always been an enriching experience for me. The subject of my PhD was to explore whether syntax could improve Statistical MT. The problem is that so far, the impact of linguistic structures has been relatively limited, and overweight by the gain from "more data" or "bigger model." In my understanding, both linguists and algorithms try to discover the regularities (and irregularities) of the language (or several languages at a time in the context of MT applications). Some of those regularities will be consistent across domains, and others will change when we switch from one domain to another (e.g., conversational language versus news articles). The algorithms of course have far more capacities to adapt to the context/domain switch given that those algorithms have access to the relevant data. Therefore, linguists would have a hard time to compete with the machines on the tasks where the data is abandoned. However, when we switch to lower resource tasks, including the translation from very low resource languages, or some translation for very specific domains, we would definitely benefit from the linguistic insights, which could guide MT developers in the design of algorithms. So to answer your question: yes, I do believe that linguists could help with MT development, when the data is limited or sometimes inexistent. If we go beyond MT tasks (which is pretty well defined), in NLP in general, I do believe that linguists' insights are precious in formulating new challenging tasks for NLP. And this is how the progress is made.

SHARON: The move to data-driven MT seemed to reduce the importance of linguistics -- and of linguists. The improvements of MT output, thanks to NMT, could be seen as limiting the role of linguists even further. However, there is another way of looking at it: to move NMT output to the next level, the issues that need to be resolved are linguistic issues (gender in language, style, register, cohesion, etc.). I think it would be a big mistake for MT developers to assume that this is just a machine learning problem that will be solved by data.

VASSILINA: I believe this work by Pierre Isabelle and colleagues from NRC Canada is a good illustration of what you are saying, Sharon. This work creates a challenging test set to evaluate the capacities of various MT systems to handle various linguistic phenomena. On the other hand, what this work and a follow-up work show is that even if not perfect, current MT systems are making progress in handling those phenomena.

I think it is not only about the data. The data itself is multidimensional. Various factors are important like the amount of data, the quality of data, and diversity of data. But it is also about the algorithms which evolve, and are able to handle more data, and get more out of the data. I would like to give as an example Papago honorific translation, a feature that allows the user to control the register of produced translation. There is a combination of data and smarter algorithms behind this feature. So to a certain extent, some of the problems cited by Sharon could be (partially) addressed by more/better data and smarter algorithms. But we definitely need more challenging datasets and better evaluation procedures to progress further. BLEU scores won't be able to trace this kind of progress.

JOST: Is there a threshold for women to get into this field? (And: What is the path to become part of it?)

LYNNE: Is the threshold specific to MT or does it apply more broadly to tech or even STEM? Are there genuinely fewer women in the field, or do women just have a lower visibility? For instance, in academia overall (across all disciplines), there are nearly as many women as men, but when you get to the senior positions (e.g., full professor), the men account for about 70-75% of the posts, and the women just 25-30% (in Canada/Ottawa, anyway). So the imbalance is not in the total but in the distribution, with men all bunched up in the senior ranks and women all bunched up in the lower ranks. And this is across all disciplines, so not really tech-specific. Generalized explanations that are given are that women are penalized by taking maternity leaves, by having or wanting to do more or the care-giving (both for children and elderly parents), or that they are paid significantly less and so don't feel motivated to work twice as hard.… But I have seen specific studies on this problem in academia and seen specific studies on gender pay gap issues, but I have never seen specific studies on the MT field.

If you want to work in MT development, you need to have some background in coding, which is not typically part of a degree in languages or translation. Maybe it should be! But at present, it's not the norm. In Canada, this usually means taking a computer science degree, or some other program that has a strong software engineering component. It's pretty common nowadays for graduate programs in computer science to offer courses in Natural Language Processing (NLP), whereas graduate programs in translation offer courses in translation technologies that focus on technology use rather than on its development. So to my mind, the most typical path to MT development would be through a computer science program, rather than through a translation program. And certainly there seem to be fewer women in computer science programs at the present time, though there are initiatives to increase the number of women in STEM fields, so hopefully we'll see progress in this regard moving forward.

VASSILINA: As far as I know, it is generally encouraged to increase diversity in the Computer Science field in general (and MT development is a subfield of Computer Science in this context). Both companies and universities strongly encourage qualified women candidates to apply to their job offers, and various programs exist to make women's presence in the field stronger. I don't think one can define any "threshold," though as I mentioned the women are already under-represented in Computer Science departments.

SHARON: Any imbalance in the field of MT mirrors the imbalance in STEM itself. In my institution, I see proactive efforts to address this issue, not just in STEM, but in computer science and in NLP specifically through the "Women in STEM" movement, for example. In fact, in Andy Way's MT lab at DCU, there are currently seven men and ten women, the latter ranging from the level of professor, through research fellow, post-doc, PhD and project staff. There is another issue here that I think is equally important, which is interdisciplinarity. As Lynne says, the route for anyone into MT development is normally through a computer science (or related) degree. Most MT developers 'see' the world through that lens. In MT development and deployment, that typically led to a focus on measuring ‘success' in computational ways (through BLEU scores, for example), using data that was limited and not checked for quality. Increases in BLEU scores (or equivalent) are emphasized rather than an impact on end users. Having people on your MT development team with linguistic, translation and HCI skills in general, means that you have a much stronger team who see the world through different lenses, which ultimately should make your science and technology more robust and more acceptable. The big issue for me is not how many women are working with MT, but how many people from different disciplines are contributing to the development and measurement of success?

JOST: What I think I'm hearing all three of you say is that people with linguistic skills would be helpful. And if that is the case, do they necessarily need the coding and STEM skills that you are mentioning? (Although, I really like Lynne's suggestion that coding should probably be part of translation programs!)

SHARON: Yes, but a considerable challenge is bridging gaps. Therefore, it makes sense for the computational people to understand translation (and translators and end users of translation) and the linguists to understand coding. We have started to address this in our MSc in Translation Technology, where our students used to take a course in Java Programming (now moving to Python). This course is delivered to students in other faculties too, and I don't mind boasting that our students do really well on this course, probably because they 'get' language. This opens the door for them into MT development companies, where they will not necessarily do coding, but they understand what's going on. Other Master's programmes have also started to introduce coding modules.

VASSILINA: I totally agree with Sharon. I think it is important that we "speak the same language" to better work together.

LYNNE: At the moment, our translation program does not include coding, although students could certainly take this as an elective. Indeed, there are some humanities coding courses offered, such as through the Digital Humanities minor, for instance. But we do not have a program specifically dedicated to translation technology, as Sharon describes at DCU. For thousands of years, universities were very discipline-based, and so language and computing were essentially in separate silos. But in the last 50 years or so, interdisciplinarity has begun to emerge. In the early days, it was mostly just lip service since the longstanding siloed structures of academia made it difficult to put into action; however, these structural hurdles are gradually disappearing, and I suspect that as universities continue to embrace interdisciplinarity, then more programs such as "translation technology" will appear. And it's in this interdisciplinary space that people can be properly supported to develop hybrid skill sets (e.g., language plus coding) and then go on to act as bridges.

JOST: Would this field look different if more women were present? Both and maybe separately in academia and industry. Would MT itself be different?

VASSILINA: I am not sure how men/women parity would transform the field of MT development. What I think would be interesting is that MT developers start to work closely with MT users (who are mostly women as mentioned by Lynne). That could definitely push MT research in a slightly different direction which could benefit both sides (MT developers and MT users).

LYNNE: Some very interesting recent work on gender bias in MT has been done by Eva Vanmassenhove, who recently completed her PhD on MT in the School of Computing at Dublin City University and who is now an assistant professor at Tilburg University in the Netherlands. Dr. Vanmassenhove looked at how corpus-based MT systems, such as neural MT systems, can perpetuate and even exaggerate any gender bias found in the training corpora. So this is a case where a woman MT researcher explored the topic of gender bias, and perhaps that's a topic that would not necessarily have been investigated as readily by a man. So thanks to this work, Google Translate has undertaken to seek ways to reduce gender bias in their system, which means that the MT output of tomorrow will hopefully look different than the MT output of today. I also agree with Vassilina that finding ways to bring MT developers and those who study user issues in MT together for more conversations is important too. And I think there actually has been improvement in this area in recent years. The MT Summit conferences, as well as some European Association for MT conferences are increasingly offering different "tracks" -- R&D track, user track, etc. -- and these offer opportunities for people with different interests in MT to come together and to share with and learn from each other. Let's hope that this trend continues!

SHARON: This is a difficult question to answer without simply speculating, but I find myself echoing the thoughts of Vassilina and Lynne. Much more emphasis has been placed recently on end user experience of MT. This has actually been driven from the translation side of the house (for obvious reasons), the majority of whom are women. We want to know how good MT is, for what types of text, domains, for which use-case scenarios, and how it impacts a wide variety of users. We are also asking questions about ethics, fair use, etc.… (though this is not only driven by women!). The field is much richer when different types of researchers and users come together. I'd like to see more of that!

JOST: I really, really want to thank you for this conversation. I learned so much. I also want to thank you all for your work and the thoughtfulness with which you conduct it.

Translating PDFs is easier and quicker with TransPDF.

Try it FREE for your next PDF project.

As a subscriber to the Premium version of this journal you have access to an archive of Premium journals going back to 2007.

You can access the archive right here. This month the user name is toolbox and the password is dirtongrass.

New user names and passwords will be announced in future journals.

Plans for the autumn? Reach new translation heights! You have enough time for new business achievements before the year is over. All you need is a smart software for translators! Benefit from the exclusive offer for Tool Box Journal readers from AIT at translation3000.com/Toolbox-Autumn-2020.

10 copies of each product available at 55% off. Get it or regret it!

Focus on translation, not administration. Grab your copy now.

If you would like to promote this electronic journal by placing a link on your website, I will in turn mention your website in a future edition of the Tool Box Journal. Just paste the code you find here into the HTML code of your webpage, and the little icon that is displayed on that page with a link to my website will be displayed.

If you are subscribed to this journal with more than one email address, it would be great if you could unsubscribe redundant addresses through the links Constant Contact offers below.

If you are interested in reprinting one of the articles in this journal for promotional purposes, please contact me for information about pricing.

© 2020 International Writers' Group