Abstract
Long-distance semantic dependencies are crucial for lexical choice in statistical machine translation. In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the context of machine translation. We incorporate preferences that verbs impose on subjects and objects into translation. In addition, bilingual selectional preferences between source-side verbs and target-side arguments are also investigated. Our experiments on Chinese-to-English translation tasks with large-scale training data demonstrate that statistical machine translation using verbal selectional preferences can achieve statistically significant improvements over a state-of-the-art baseline.- Anthology ID:
- C16-1203
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2154–2163
- Language:
- URL:
- https://aclanthology.org/C16-1203
- DOI:
- Cite (ACL):
- Haiqing Tang, Deyi Xiong, Min Zhang, and Zhengxian Gong. 2016. Improving Statistical Machine Translation with Selectional Preferences. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2154–2163, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Improving Statistical Machine Translation with Selectional Preferences (Tang et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/C16-1203.pdf