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Computer Science @ Rochester
Monday, April 28, 2008
3:30 PM
Computer Studies Bldg. Room 703
Ph.D. Thesis Proposal
Shaojun Zhao
University of Rochester
Applying Word Alignment to Automatic Machine Translation Evaluation
All statistical machine translation (MT) systems are built on the idea of word alignment. The HMM based word alignment model and the IBM model 4 play the central role in word alignment. The HMM model has an efficient algorithm, while the IBM model 4 has to perform sampling. We propose a model that has a simple, exact dynamic programming algorithm, and achieves better performance than the HMM model. Automatic machine translation evaluation has revolutionized the development of MT systems. The BLEU and NIST metric are the de-facto standards. They are both based on N-gram overlap. Many other metrics have been proposed based on the longest common subsequence. We propose several metrics including a multidimensional alignment approach which is likely to solve many of the drawbacks of the current metrics.