Part-of-Speech Induction in Dependency Trees for Statistical Machine Translation

Akihiro Tamura, Taro Watanabe, Eiichiro Sumita, Hiroya Takamura and Manabu Okumura

The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

This paper proposes a nonparametric Bayesian method for inducing Part-of-Speech (POS) tags in dependency trees to improve the performance of statistical machine translation (SMT). In particular, we extend the monolingual infinite tree model (Finkel et al., 2007) to a bilingual scenario: each hidden state (POS tag) of a source-side dependency tree emits a source word together with its aligned target word, either jointly (joint model), or independently (independent model). Evaluations of Japanese-to-English translation on the NTCIR-9 data show that our induced Japanese POS tags for dependency trees improve the performance of a forest-to-string SMT system. Our independent model gains over 1 point in BLEU by resolving the sparseness problem introduced in the joint model.

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