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. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score 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.- Anthology ID:
- P17-2021
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 132–140
- Language:
- URL:
- https://aclanthology.org/P17-2021
- DOI:
- 10.18653/v1/P17-2021
- Cite (ACL):
- Roee Aharoni and Yoav Goldberg. 2017. Towards String-To-Tree Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 132–140, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Towards String-To-Tree Neural Machine Translation (Aharoni & Goldberg, ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P17-2021.pdf
- Data
- WMT 2016