Pushing the Limits of Translation Quality Estimation

André F. T. Martins, Marcin Junczys-Dowmunt, Fabio N. Kepler, Ramon Astudillo, Chris Hokamp, Roman Grundkiewicz

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways. However, this potential is currently limited by the relatively low accuracy of existing systems. In this paper, we achieve remarkable improvements by exploiting synergies between the related tasks of word-level quality estimation and automatic post-editing. First, we stack a new, carefully engineered, neural model into a rich feature-based wordlevel quality estimation system. Then, we use the output of an automatic post-editing system as an extra feature, obtaining striking results on WMT16: a word-level FMULT 1 score of 57.47% (an absolute gain of +7.95% over the current state of the art), and a Pearson correlation score of 65.56% for sentence-level HTER prediction (an absolute gain of +13.36%).
Original languageEnglish
Pages (from-to)205-218
Number of pages14
JournalTransactions of the Association for Computational Linguistics
Volume5
Publication statusPublished - 1 Jul 2017

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