Incremental adaptation using translation informations and post-editing analysis

This is an accepted manuscript of an article published by IWSLT in Proceedings of the International Workshop on Spoken Language Translation 2012, available online: http://hltc.cs.ust.hk/iwslt/proceedings/Proceedings_Iwslt2012.pdf The accepted version of the publication may differ from the final published version.It is well known that statistical machine translation systems perform best when they are adapted to the task. In this paper we propose new methods to quickly perform incremental adaptation without the need to obtain word-by-word alignments from GIZA or similar tools. The main idea is to use an automatic translation as pivot to infer alignments between the source sentence and the reference translation, or user correction. We compared our approach to the standard method to perform incremental re-training. We achieve similar results in the BLEU score using less computational resources. Fast retraining is particularly interesting when we want to almost instantly integrate user feed-back, for instance in a post-editing context or machine translation assisted CAT tool. We also explore several methods to combine the translation models

Leveraging online user feedback to improve statistical machine translation

In this article we present a three-step methodology for dynamically improving a statistical machine translation (SMT) system by incorporating human feedback in the form of free edits on the system translations. We target at feedback provided by casual users, which is typically error-prone. Thus, we first propose a filtering step to automatically identify the better user-edited translations and discard the useless ones. A second step produces a pivot-based alignment between source and user-edited sentences, focusing on the errors made by the system. Finally, a third step produces a new translation model and combines it linearly with the one from the original system. We perform a thorough evaluation on a real-world dataset collected from the Reverso.net translation service and show that every step in our methodology contributes significantly to improve a general purpose SMT system. Interestingly, the quality improvement is not only due to the increase of lexical coverage, but to a better lexical selection, reordering, and morphology. Finally, we show the robustness of the methodology by applying it to a different scenario, in which the new examples come from an automatically Web-crawled parallel corpus. Using exactly the same architecture and models provides again a significant improvement of the translation quality of a general purpose baseline SMT system

Interactive and Adaptive Neural Machine Translation

In this dissertation, we examine applications of neural machine translation to computer aided translation, with the goal of building tools for human translators. We present a neural approach to interactive translation prediction (a form of "auto-complete" for human translators) and demonstrate its effectiveness through both simulation studies, where it outperforms a phrase-based statistical machine translation approach, and a user study. We find that about half of the translators in the study are faster using neural interactive translation prediction than they are when post-editing output of the same underlying machine translation system, and most translators express positive reactions to the tool. We perform an analysis of some challenges that neural machine translation systems face, particularly with respect to novel words and consistency. We experiment with methods of improving translation quality at a fine-grained level to address those challenges. Finally, we bring these two areas -- interactive and adaptive neural machine translation -- together in a simulation that shows that their combination has a positive impact on novel word translation and other metrics