Paper: Extentions To HMM-Based Statistical Word Alignment Models

ACL ID W02-1012
Title Extentions To HMM-Based Statistical Word Alignment Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2002
Authors

This paper describes improved HMM-based word level alignment models for statistical machine translation. We present a method for using part of speech tag information to improve alignment accu- racy, and an approach to modeling fertility and cor- respondence to the empty word in an HMM align- ment model. We present accuracy results from eval- uating Viterbi alignments against human-judged alignments on the Canadian Hansards corpus, as compared to a bigram HMM, and IBM model 4. The results show up to 16% alignment error reduc- tion.