Abstract
Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English–German language pair.- Anthology ID:
- N18-2078
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 486–492
- Language:
- URL:
- https://aclanthology.org/N18-2078
- DOI:
- 10.18653/v1/N18-2078
- Cite (ACL):
- Diego Marcheggiani, Jasmijn Bastings, and Ivan Titov. 2018. Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 486–492, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks (Marcheggiani et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/N18-2078.pdf
- Data
- WMT 2016