NDLI: Automatic machine translation error identification

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Automatic machine translation error identification

Content Provider	Springer Nature Link
Author	Jesus Martins, Débora Beatriz Medeiros Caseli, Helena
Copyright Year	2014
Abstract	Although machine translation (MT) has been an object of study for decades now, the texts generated by the state-of-the-art MT systems still present several errors for many language pairs. Aiming at coping with this drawback, lots of efforts have been made to post-edit those errors either manually or automatically. Manual post-editing is more accurate but can be prohibitive when too many changes have to be made. Automatic post-editing demands less effort but can also be less effective and give rise to new errors. A way to avoid unnecessary automatic post-editing and new errors is by previously selecting only the machine-translated segments that really need to be post-edited. Thus, this paper describes the experiments carried out to automatically identify MT errors generated by a state-of-the-art phrase-based statistical MT system. Despite the fact that our experiments have been carried out using a statistical MT engine, we believe the approach can also be applied to other types of MT systems. The experiments investigated the well-known machine-learning algorithms Naive Bayes, Decision Trees and Support Vector Machines. Using the decision tree algorithm it was possible to identify wrong segments with around 77 % precision and recall when a small training corpus of only 2,147 error instances was used. Our experiments were performed on English-to-Brazilian Portuguese MT, and although some of the features are language-dependent, the proposed approach is language-independent and can be easily generalized to other language pairs.
Starting Page	1
Ending Page	24
Page Count	24
File Format	PDF
ISSN	09226567
Journal	Machine Translation
Volume Number	29
Issue Number	1
e-ISSN	15730573
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-11-28
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Automatic error identification Automatic post-edition Machine translation Machine learning Language Translation and Linguistics Computational Linguistics Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software Linguistics and Language

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