Abstract
This paper introduces deep syntactic structures to syntax-based Statistical Machine Translation (SMT). We use a Head-driven Phrase Structure Grammar (HPSG) parser to obtain the deep syntactic structures of a sentence, which include not only a fine-grained syntactic property description but also a semantic representation. Considering the abundant information included in the deep syntactic structures, it is interesting to investigate whether or not they improve the traditional syntax-based translation models based on PCFG parsers. In order to use deep syntactic structures for SMT, this paper focuses on extracting tree-to-string translation rules from aligned HPSG tree–string pairs. The major challenge is to properly localize the non-local relations among nodes in an HPSG tree. To localize the semantic dependencies among words and phrases, which can be inherently non-local, a minimum covering tree is defined by taking a predicate word and its lexical/phrasal arguments as the frontier nodes. Starting from this definition, a linear-time algorithm is proposed to extract translation rules through one-time traversal of the leaf nodes in an HPSG tree. Extensive experiments on a tree-to-string translation system testified the effectiveness of our proposal.
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Wu, X., Matsuzaki, T. & Tsujii, J. Improve syntax-based translation using deep syntactic structures. Machine Translation 24, 141–157 (2010). https://doi.org/10.1007/s10590-010-9081-6
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DOI: https://doi.org/10.1007/s10590-010-9081-6