Abstract
In syntax-directed translation, the source-language input is first parsed into a parse-tree, which is then recursively converted into a string in the target-language. We model this conversion by an extended tree-to-string transducer that has multi-level trees on the source-side, which gives our system more expressive power and flexibility. We also define a direct probability model and use a linear-time dynamic programming algorithm to search for the best derivation. The model is then extended to the general log-linear frame-work in order to incorporate other features like n-gram language models. We devise a simple-yet-effective algorithm to generate non-duplicate k-best translations for n-gram rescoring. Preliminary experiments on English-to-Chinese translation show a significant improvement in terms of translation quality compared to a state-of-the- art phrase-based system.- Anthology ID:
- 2006.amta-papers.8
- Volume:
- Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers
- Month:
- August 8-12
- Year:
- 2006
- Address:
- Cambridge, Massachusetts, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 66–73
- Language:
- URL:
- https://aclanthology.org/2006.amta-papers.8
- DOI:
- Cite (ACL):
- Liang Huang, Kevin Knight, and Aravind Joshi. 2006. Statistical Syntax-Directed Translation with Extended Domain of Locality. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 66–73, Cambridge, Massachusetts, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Statistical Syntax-Directed Translation with Extended Domain of Locality (Huang et al., AMTA 2006)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2006.amta-papers.8.pdf