Forest-Based Neural Machine Translation
Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Tiejun Zhao, Eiichiro Sumita
Abstract
Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forest-based methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest under a simple sequence-to-sequence framework (i.e., a forest-to-sequence NMT model). The BLEU score of the proposed method is higher than that of the sequence-to-sequence NMT, tree-based NMT, and forest-based SMT systems.- Anthology ID:
- P18-1116
- Original:
- P18-1116v1
- Version 2:
- P18-1116v2
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1253–1263
- Language:
- URL:
- https://aclanthology.org/P18-1116
- DOI:
- 10.18653/v1/P18-1116
- Cite (ACL):
- Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Tiejun Zhao, and Eiichiro Sumita. 2018. Forest-Based Neural Machine Translation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1253–1263, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Forest-Based Neural Machine Translation (Ma et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/P18-1116.pdf