Promoting a “Healthy” MT Implementation in Translation Courses

Emmanouela Patiniotaki
University College London

 

MT has often been seen as a threat to translators. In many instances, professionals and academics have interpreted it as an attempt to lower translators’ rates, attack the profession, replace humans with machines, and undermine quality. It cannot be denied that MT can have a negative impact on employability, challenge the viability of the profession in its traditional form (i.e. pure human translation), as well as compromise the quality of the translation output and the idea of translation as a high-profile task performed by skilled professionals. However, MT is the logical sequence in technological advancements and, as such, it should be utilised rather than demonised.

 

Students of Machine Translation

 

The task of introducing MT in educational translation programmes as a tool for translators rather than as feature of a CAT tool that could be ignored requires careful planning and it should be based on certain principles. These principles are necessary to clarify the grounds on which such a solution is introduced to students, the usefulness of the material presented to them, the possible applications and, most importantly, the way in which MT systems should be handled. Teaching MT is different from teaching any other CAT tool in the sense that a MT system intervenes in the mental process of the translator. However, since CAT tools also include MT plug-ins and support APIs from various MT systems, it is better to guide translation students or trainees into a suggested way of using MT (based on professional experience) in the most effective manner, rather than unintentionally expose them to a new technological advancement without any preparation.

MT has become a widely used tool in everyday life, mainly through applications installed in various devices and free MT systems on the Web, basically for information and communication purposes. This exposure and familiarity with open-source MT engines brings people closer to the idea of MT, making it a tool itself and perhaps resulting in future translators adopting a more welcoming approach to this technology. Thus, the risks of not defining the setting of MT within the profession are many. The two sides –those already exposed to MT engines for different purposes (e.g. instant website translation) and those who see it as a risk itself for the profession– make a discussion on MT within educational contexts a prerequisite before actually using such a system in professional contexts.

In order to set the grounds for the purposes of introducing MT to translation students and teaching them how to include it effectively in the translation process, it is important to: a) discuss the history and nature of MT systems as well as relevant research on the field, the different types of MT and the purposes they serve; b) discuss the usefulness of MT systems within the field of translation; c) establish connections with Linguistics and the ways in which language is handled by MT systems, syntactically, grammatically and semantically; d) define MT problems and focus on the translator’s role in customisation, pre-editing, post-editing and retraining of systems; e) practise using MT systems with tasks and assignments designed bearing in mind scenarios that can and do exist within the translation industry.

 

 

University Machine Translation

 

 

With regard to the principles for such an attempt, the following non-exhaustive are proposed:

  • MT systems should not be seen as replacements, but rather as tools in translation training and the profession in general.

 

  • They can be integrated or stand-alone and, as such, they can have limitations but also offer customisation options.

 

  • They require training and practice.

 

  • MT systems are gradually becoming a reality in the profession and knowing how to handle them best can be an asset, especially for new translators.

 

  • MT systems become more effective when their purposes are better defined based on usage, as is the case with domain-specific engines.

 

  • Metrics are a good way to evaluate a system, yet each translation job is different and several parameters (including client requests, data quality, purpose of translation, etc.) need to be considered when assessing those metrics.

 

  • MT systems are (in most of the cases) constantly updated engines, and translators ought to be informed about their structure, content and source of training data.

 

  • The field of MT is very fruitful for conducting research and for carrying out case-studies within Translation and other fields.

 

  • MT solutions should be used wisely and critically. Pre-translation with MT output should be very carefully considered and always in relation to the data a system has been trained with. Substitution of the translator’s originality in translating empty segments with pre-translations consisting of MT raw output can result in non-genuine and very homogenous (loss of identity among different translation sources) translation performance.

 

Based on the above, students can be driven through what can be seen as a “healthy” implementation of MT systems in translator training. Instead of being faced with the reality of pre-translated documents that require post-editing, the need to adjust to a company’s MT practices, or even the need to develop their own systems as freelancers to improve their effectiveness in terms of time and cohesion, they can be prepared and gradually guided with the use of existing solutions that will make them experience the advantages and disadvantages of MT systems and also test how they could be included in their list of translation tools. After all, the choice of translation tools, when it is not dictated by vendors or companies, is a personal one and, like any other choice in this context, it is based on preference and convenience. Finally, knowledge of a system’s behaviour from the inside is always an advantage for its most effective use.

KantanMT has been used at UCL this year to teach students at master’s level. It is ideal for teaching purposes due to its highly customisable nature. Students had the chance to build domain-specific engines (mainly technical), train them with data they collected from other tools, translate documents, apply rules and perform post-editing tasks. They managed the metrics to realise how systems perform based on the data fed into them, which was very useful as it is often hard for people with a linguistic background to realise how a perfectly well-written set of data may not suffice for good training. Students also learned that the relations between the system structure, the source and target languages, and the content of the translation file play a crucial role as regards the effectiveness of a MT system. All these line up with the teaching approach explained above and facilitate the attempt to introduce a positive view towards MT by translators themselves, based on the idea of MT being used as a tool that should be perceived very critically and managed very carefully in order for it to be effective in the translation process.

It is important to notice that students had the opportunity to express their thoughts on the system through an online questionnaire and also through their assignments. The aim of the module was not to persuade them about the usefulness of MT, but rather to provide guidance and to help them realise its place in the industry, its advantages and disadvantages, as well as train them for the successful handling of MT systems in a professional environment. The system will also be used in professional translators’ training and online courses in next academic year with the hope to familiarise participants with a side of Translation Technology that is often left out of teaching contexts.

With thanks to Rocío Baños Pinero.

3 thoughts on “Promoting a “Healthy” MT Implementation in Translation Courses

  1. It’s good news to see that translator training programmes, which have traditionally largely overlooked MT technology, are starting to catch up and now prepare future translators for real-world industry settings. I myself got good exposure to MT within an MA course and internship (Swansea University, and we also used KantanMT for a hands-on session). With the boom MT has been experiencing lately, handling MT systems is bound to become part of the translator’s mainstream, standard skills. Just one comment: free translation services such as Google Translate, Microsoft Translator or Systran rely on anything but ‘open-source’ software; and one question: how did you go about analysing how language is handled by MT systems, and how deep did you go with this analysis? This, I think, might be where the crux of the issue lies: there would be greater acceptance of MT by translators if they were allowed a deeper insight into the mechanisms of the system, but unfortunately – I think it was Daniel Marcu who put it this way – you need a PhD in computer science to really understand what’s going on in there and be able to handle open-source systems such as Moses… translators are simply left out of the development of the tools they are expected to use.

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  2. Dear Octavia,

    Thank you for your comment. In fact the particular MSc focuses very much on the practical side of the profession. It is an MSc in Scientific, Technical and Medical Translation with Translation Technologies and we only moved to UCL last year as we used to be based at Imperial College. So for us keeping up with technology is a must. We have been teaching MT as part of a Language & Automation module for years and we have always been focusing on the translator’s point of view.

    With regard to open-source systems, I was referring to free MT apps basically that are available on the Internet rather that Systran, Moses or Microsoft or APIs. (I hope I am addressing your comment in the right direction.)

    As for the linguistic aspects involved in teaching MT in this particular context, although we discussed the different types of MT systems and what each system practically does in order to produce the output, we mostly focused on what the translator should do at the stages of pre-editing (controlled language etc) and post-editing (quality levels etc.) and how to evaluate the MT output which can be interpreted in a manner that indicates how the system actually works.

    However, the students did not need to have computer science knowledge in order to handle the system, but consider that we are talking about computer-literate users with experience in translation technology. What I decided to do is give extra information and chances to develop aspects that involve a little bit of programming as optional parts in assignments, but always after a relevant demonstration.

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  3. Thanks for your answer, Emmanouela. The module you’ve described sounds extremely well designed and indeed seems to cover all the practical aspects of handling MT. Let’s hope this kind of approach becomes generalised in translation courses.

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