Abstract
Most neural machine translation models only rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. In this work, we investigate different approaches to incorporate syntactic knowledge in the Transformer model and also propose a novel, parameter-free, dependency-aware self-attention mechanism that improves its translation quality, especially for long sentences and in low-resource scenarios. We show the efficacy of each approach on WMT English-German and English-Turkish, and WAT English-Japanese translation tasks.- Anthology ID:
- 2020.acl-main.147
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1618–1627
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.147
- DOI:
- 10.18653/v1/2020.acl-main.147
- Cite (ACL):
- Emanuele Bugliarello and Naoaki Okazaki. 2020. Enhancing Machine Translation with Dependency-Aware Self-Attention. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1618–1627, Online. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Machine Translation with Dependency-Aware Self-Attention (Bugliarello & Okazaki, ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.acl-main.147.pdf
- Code
- e-bug/pascal