Just recently two major initiatives have emerged (or are in the process of emerging). TAUS is promoting the “Human Language Project” and LT-Innovate is initiating the development of “European Language Cloud” (ELC). Both of these projects have the same main idea: sharing language technologies and data in a way that enables faster development and easier use of various language resources in many languages. Continue reading Big Ideas in the European Language Industry: “European Language Cloud” and “Human Language Project”
Since we introduced a technology that can estimate the reliability and quality of machine translation, many people have asked me why it is important to know the quality of the machine translations. I’d like to compare the situation to a more concrete situation, namely to a car and its parts. Think about the situation where you can buy car parts or components that are made by either human professionals or automatic machines. Continue reading Why Should We Know Whether We Can Trust MT?
Machine translation (MT) is an emerging technology, and there still seems to be a lot of confusion of what can be done with automatic translation. This article highlights two useful ways to use MT. Continue reading 2 Efficient Ways to Use Machine Translation
It is usually very difficult to estimate or evaluate the quality of automatic translations. The main reason for this is that typically the user translates a piece of text either from or to a language that he doesn’t understand good enough. (If he did understand, then he wouldn’t need to translate it.) Thus he cannot really know whether the translation contains errors or not.
The traditional way to get an understanding of the machine translation quality has involved comparing the machine translation with a translation made by a professional translator. This has been done mainly for research and development purposes by the developers of machine translation technology. However, this kind of method is not useful in real-life situations because usually there isn’t available any professional translation for material which needs to be machine translated.
There is definitely a demand for a technology for automatic estimation of the quality of machine translations. The general idea behind such a technology would be creating a system which would automatically tell the user whether some machine translation is good or bad or something between. This kind of technology would benefit us in several ways:
- With reliable machine translation quality estimates automatic translations can be used in more demanding situations. Currently machine translation is often used for translating information where translations errors would cause only minimal or no harm at all. This has seriously limited the use of automatic translation.
- Bad translations can be filtered out of a post-editing process. The post-editing can be made more efficiently because the post-editor does not need the spend time with useless translations.
- Deciding whether the machine translation is good enough for publishing will become easier.
- The system can be used to select the best among several machine translations. This will obviously improve the overall perceived machine translation quality as well as avoid embarrassing mistakes.
Lately we at Multilizer have made significant steps in estimating machine translation quality automatically. The quality estimation will be also one of the subjects in a scientific workshop about machine translation in Montreal at the beginning of June.
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In association with Kites, Finnish machine translation experts and enthusiasts have formed Special Interest Group for machine translation in Finland. In one meeting we handled a hot subject of post-editing machine translations. Post-editing means that a professional translator checks and edits automatic translations which are made by a machine. The interesting topic with excellent presentations (one made by Jukka Outinen of Lionbridge and another by freelance translator Tommi Nieminen) sparked a lively discussion.
One of the ideas highlighted during the meeting was that in traditional translation by a professional translator, it does not make much sense to lower one’s quality requirements. It does not improve productivity. A professional translator cannot choose to “write bad translations”. With professional translators, the style and fluency of the text come together with the translation. However, the situation is different when post-editing machine translations.
In post-editing machine translations it can make sense to lower quality requirements because that indeed improves productivity. When the task is to post-edit an automatic translation, the translator can choose not to correct those parts of the machine translation that are correct but written in a clumsy language. Thus the translator saves some time at the expense of the quality. Therefore, in post-editing machine translations the quality can indeed be traded for productivity.
This naturally changes the translation market. Clients can now choose between lower and higher quality, depending on his requirements and budget. Affordable, quick and good enough translations made by a machine and a man together are fulfilling the scale of available translation services. An increase in productivity will enlarge the entire translation industry.
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Often usefulness of machine translation quality is regarded as the same thing as its quality. However, machine translation can be used in many ways. And in different use cases different things matter. Here we represent one case where translation’s usefulness depends only partially on the machine translation quality.
The example is about a newsletter which was machine translated to different languages. The newsletter recipients were asked to evaluate both the quality of the translation and how useful they think the translation is. (Read the complete case study here.) The graph below shows the results.
These results are quite interesting. The graph shows that although the machine translation quality was evaluated far from perfect, the translations’ usefulness was regarded as higher than its quality. However, this applies only when translation quality is above certain threshold. Bad or poor quality machine translations are naturally deemed as useless.
Although this case study is rather old, it seems that the respondents were rather tolerant towards translation errors. The quality of machine translation is nowadays quite different than it was in the beginning of the 21st century. It’s hard to tell what were the respondents general conceptions about the overall quality of machine translation. It is probable that today the use of free machine translation services has taught people to expect even less from machine translation.
The most important thing here is to realize that the quality (or the lack of it) doesn’t directly determine the usefulness of the machine translation. The quality of machine translation is more than just correct grammar.
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