Indirect-HMM-based Hypothesis Alignment for Combining Outputs from Machine Translation Systems.

Xiaodong He,Mei Yang,Jianfeng Gao,Patrick Nguyen,Robert Moore

Conference on Empirical Methods in Natural Language Processing (EMNLP)（2008）CCF B

被引用105|浏览9

摘要

This paper presents a new hypothesis alignment method for combining outputs of multiple machine translation (MT) systems. An indirect hidden Markov model (IHMM) is proposed to address the synonym matching and word ordering issues in hypothesis alignment. Unlike traditional HMMs whose parameters are trained via maximum likelihood estimation (MLE), the parameters of the IHMM are estimated indirectly from a variety of sources including word semantic similarity, word surface similarity, and a distance-based distortion penalty. The IHMM-based method significantly outperforms the state-of-the-art TER-based alignment model in our experiments on NIST benchmark datasets. Our combined SMT system using the proposed method achieved the best Chinese-to-English translation result in the constrained training track of the 2008 NIST Open MT Evaluation.

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关键词

IHMM-based method,alignment method,hypothesis alignment,proposed method,state-of-the-art TER-based alignment model,word semantic similarity,word surface similarity,Chinese-to-English translation result,NIST Open MT Evaluation,NIST benchmark datasets,Indirect-HMM-based hypothesis alignment,machine translation system