Abstract
We investigate the impact of parse quality on a syntactically-informed statistical machine translation system applied to technical text. We vary parse quality by varying the amount of data used to train the parser. As the amount of data increases, parse quality improves, leading to improvements in machine translation output and results that significantly outperform a state-of-the-art phrasal baseline. © 2006 Association for Computational Linguistics.
Cite
CITATION STYLE
Quirk, C., & Corston-Oliver, S. (2006). The impact of parse quality on syntactically-informed statistical machine translation. In COLING/ACL 2006 - EMNLP 2006: 2006 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 62–69). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1610075.1610085
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