A linguistically annotated reordering model for BTG-based statistical machine translation

Authors:
Deyi Xiong;Min Zhang;Aiti Aw;Haizhou Li
Affiliations:
Institute for Infocomm Research, Heng Mui Keng Terrace, Singapore;Institute for Infocomm Research, Heng Mui Keng Terrace, Singapore;Institute for Infocomm Research, Heng Mui Keng Terrace, Singapore;Institute for Infocomm Research, Heng Mui Keng Terrace, Singapore
Venue:
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Year:
2008

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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.