Statistical Syntax-Directed Translation with Extended Domain of Locality

Liang Huang, Kevin Knight, Aravind Joshi


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2006.amta-papers.8.pdf