by Rubén de la Fuente
I find it very disheartening that every time translators discuss MT is either to insist it can’t replace humans or to laugh at its flaws, instead of exploring its potential (to boost productivity and profitability) and its threats (shift in business and compensation models are taking place now and it will not be in our best interest if we don’t get involved as soon as possible). MT is a game changer and in order to adjust, here are a few things every translator should know:
- It’s a mistake to speak about MT in general: output can be very different depending on the system used (and hence more or less useful). Google Translate is not as representative of the state of the art as most people think. Google is generic (not customized per domain) and cannot be customized by users, while other systems can be.
Rule-based MT systems have built-in grammars and dictionaries. They are easier to customize for people without a technical background. Their output is generally grammatically correct, but less fluent. Statistical MT systems analyze large bilingual corpora and use probability theory to suggest the most likely translation. Its output is generally more fluent, but they are more difficult to customize for people without a technical background.
- Don’t be fooled by first impressions: many translators claim it takes less to translate from scratch than to post edit, but I don’t think many have actually done the experiment. In order to have a good idea of MT potential, you should use a domain-customized system, edit a sample of significant size (500 words or more), time yourself and see the amount and kind of edits (you can use open source SymEval for that). Then you can properly analyze if you have a business case for using MT or not.
- MT will not steal jobs; instead, it can bring more work. Companies will only use unedited MT for content they would not send to human translation anyway (internal emails, highly technical content in Knowledge bases). Some companies re-invest the savings obtained with MT in buying more translations. MT developers or companies using MT will also need to hire linguists for several tasks: evaluating engines, post-edit, help improve engines.
- It can be fun to PE. The post editor is not only there to clean the machine’s mess. S/he can give very valuable feedback to help the system improve. I find it rewarding when I manage to tweak a system so that it will handle properly a certain structure it would choke on before.
Rubén R. de la Fuente has a BA in translation and interpreting from the University of Granada. He has over 10 years of experience in localization in various capacities, including as a freelance and in-house translator, reviewer, project manager, and machine translation specialist. He is currently taking a graduate course on computational linguistics. He has taught several courses and workshops about translation tools for the Universidad Alfonso X and organizations such as the Institute of Localisation Professionals, ProZ, and ecpdwebinars.co.uk. He has written articles on translation tools for ATA’s Language Technology Division. You can reach Rubén at firstname.lastname@example.org.