Using machine translation is easy; using it critically requires some thought.
Tick tock! As translators, we’re all too familiar with the experience of working under pressure to meet tight deadlines. We may have various tools that can help us to work more quickly, such as translation memory systems, terminology management tools, and online concordancers. Sometimes, we may even find it helpful to run a text segment through a machine translation (MT) system.
There was a time when translators would have been embarrassed to admit “resorting” to MT because these tools often produced laughable rather than passable results. But MT has come a long way since its post-World War II roots. Early rule-based approaches, where developers tried to program MT systems to process language similar to the way people do (i.e., using grammar rules and bilingual lexicons) have been largely set aside. Around the turn of the millennium, statistics rather than linguistics came into play, and new statistical machine translation (SMT) approaches allowed computers to do what they’re good at: number crunching and pattern matching. With SMT, translation quality got noticeably better, and companies such as Google and Microsoft, among others, released free online versions of their MT tools.
Neural Machine Translation: A game changer
In late 2016, the underlying approach to MT changed again. Now state-of-the-art MT systems use artificial neural networks coupled with a technique known as machine learning. Developers “train” neural machine translation (NMT) systems by feeding them enormous parallel corpora that contain hundreds of thousands of pages of previously translated texts. In a way, this should make translators feel good! Rather than replacing translators, NMT systems depend on having access to very large volumes of high quality translation in order to function. Without these professionally translated corpora, NMT systems would not be able to “learn” how to translate. Although the precise inner workings of NMT systems remain mysterious, the quality of the output has, for the most part, improved.
It’s not perfect, and no reasonable person would claim that it is better than the work of a professional translator. However, it would be short-sighted of translators to dismiss this technology, which has become more or less ubiquitous.
MT Literacy: Be a savvy MT user
Today, there should be no shame in consulting an MT system. Even if the suggested translation can’t be used “as is,” a translator might be able to fix it up quickly, or might simply be inspired by it on the way to producing a better translation. However, as with any tool, it pays to understand what you are dealing with. It’s always better to be a savvy user than not. Thinking about whether, when, why, and how to use MT is part of what we term “MT literacy.” It basically comes down to being an informed and critical user of this technology, rather than being someone who just copies, pastes and clicks. So what should savvy translators know about using free online MT systems?
— Information entered into a free online MT system doesn’t simply “disappear” once you close the window. Rather, the companies that own the MT system (e.g., Google, Microsoft) might keep the data and use it for other purposes. Don’t enter sensitive or confidential information into an online MT system. For more tips on security and online MT, see Don DePalma’s article in TC World magazine.
— Consider the notion of “fit-for-purpose” when deciding whether an MT system could help. Chris Durban and Alan Melby prepared a guide for the ATA entitled Translation: Buying a non-commodity in which they note that one of the most important criteria to consider is:
The purpose of the translation: Sometimes all you want is to get (or give) the general idea of a document (rough translation); in other cases, a polished text is essential.
The closer you are to needing a rough translation, the more likely it is that MT can help. As you move closer towards needing a polished translation, MT may still prove useful, but it’s likely that you are going to need to invest more time in improving the output. Regardless, it’s always worth keeping the intended purpose of the text in mind. Just as you wouldn’t want to under-deliver by offering a client a text that doesn’t meet their needs, there’s also no point in over-delivering by offering them a text that exceeds their needs. By over-delivering, you run the risk of doing extra work for free instead of using that time to work on another job or to take a well-earned break!
— Not all MT systems are the same. Each NMT system is trained using different corpora (e.g., different text types, different language pairs, different number of texts), which means they could be “learning” different things. If one system doesn’t provide helpful information, another one might. Also, these systems are constantly learning. If one doesn’t meet your needs today, try it again next month and the results could be different. Some free online MT systems include:
— Check the MT output carefully before deciding to use it. Whereas older MT systems tended to produce text that was recognizably “translationese,” a study involving professional translators that was carried out by Sheila Castilho and colleagues in 2017 found that newer NMT systems often produce text that is more fluent and contains fewer telltale errors such as incorrect word order. But just because the NMT output reads well doesn’t mean that it’s accurate or right for your needs. As a language professional, it’s up to you to be vigilant and to ensure that any MT output that you use is appropriate for and works well as part of your final target text.
Lynne Bowker, PhD, is a certified French to English translator with the Association of Translators and Interpreters of Ontario, Canada. She is also a full professor at the School of Translation and Interpretation at the University of Ottawa and 2019 Researcher-in-Residence at Concordia University Library where she is leading a project on Machine Translation Literacy. She has published widely on the subject of translation technologies and is most recently co-author of Machine Translation and Global Research (2019, Emerald).