Abstract
We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. Experiments on the WMT16 German-English news translation task shown improved BLEU scores when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.
| Original language | English |
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| Title of host publication | ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers) |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 132-140 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781945626760 |
| DOIs | |
| State | Published - 2017 |
| Event | 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada Duration: 30 Jul 2017 → 4 Aug 2017 |
Publication series
| Name | ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) |
|---|---|
| Volume | 2 |
Conference
| Conference | 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 |
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| Country/Territory | Canada |
| City | Vancouver |
| Period | 30/07/17 → 4/08/17 |
Bibliographical note
Publisher Copyright:© 2017 Association for Computational Linguistics.