Projects per year
Abstract
Analysing translation quality in regards to specific linguistic phenomena has historically been difficult and time-consuming. Neural machine translation has the attractive property that it can produce scores for arbitrary translations, and we propose a novel method to assess how well NMT systems model specific linguistic phenomena such as agreement over long distances, the production of novel words, and the faithful translation of polarity. The core idea is that we measure whether a reference translation is more probable under a NMT model than a contrastive translation which introduces a specific type of error. We present LingEval971, a large-scale data set of 97 000 contrastive translation pairs based on the WMT English→German translation task, with errors automatically created with simple rules. We report results for a number of systems, and find that recently introduced character-level NMT systems perform better at transliteration than models with byte-pair encoding (BPE) segmentation, but perform more poorly at morphosyntactic agreement, and translating discontiguous units of meaning.
Original language | English |
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Title of host publication | Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers |
Place of Publication | Valencia, Spain |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 376-382 |
Number of pages | 7 |
ISBN (Print) | 978-1-945626-34-0 |
Publication status | Published - 7 Apr 2017 |
Event | 15th EACL 2017 Software Demonstrations - Valencia, Spain Duration: 3 Apr 2017 → 7 Apr 2017 http://eacl2017.org/ http://eacl2017.org/index.php |
Conference
Conference | 15th EACL 2017 Software Demonstrations |
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Abbreviated title | EACL 2017 |
Country/Territory | Spain |
City | Valencia |
Period | 3/04/17 → 7/04/17 |
Internet address |
Fingerprint
Dive into the research topics of 'How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs'. Together they form a unique fingerprint.Projects
- 2 Finished
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SUMMA - Scalable Understanding of Mulitingual Media
Renals, S., Birch-Mayne, A. & Cohen, S.
1/02/16 → 31/01/19
Project: Research
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