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

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.

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Xiaodong He, Mei Yang, Jianfeng Gao, Patrick Nguyen, Robert Moore
• Keywords: EMNLP 2008, Hypothesis Alignment, Hypothesis Alignment Method, Natural Language Processing, Word Semantic Similarity