Abstract
In this paper, we propose a linguistically annotated reordering model for BTG-based statistical machine translation. The model incorporates linguistic knowledge to predict orders for both syntactic and non-syntactic phrases. The linguistic knowledge is automatically learned from source-side parse trees through an annotation algorithm. We empirically demonstrate that the proposed model leads to a significant improvement of 1.55% in the BLEU score over the baseline reordering model on the NIST MT-05 Chinese-to-English translation task. © 2008 Association for Computational Linguistics.
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CITATION STYLE
Xiong, D., Zhang, M., Aw, A., & Li, H. (2008). A linguistically annotated reordering model for BTG-based statistical machine translation. In ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 149–152). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1557690.1557731
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