Gappy Phrasal Alignment By Agreement

We propose a principled and efficient phraseto-phrase alignment model, useful in machine translation as well as other related natural language processing problems. In a hidden semiMarkov model, word-to-phrase and phraseto-word translations are modeled directly by the system. Agreement between two directional models encourages the selection of parsimonious phrasal alignments, avoiding the overfitting commonly encountered in unsupervised training with multi-word units. Expanding the state space to include “gappy phrases” (such as French ne pas) makes the alignment space more symmetric; thus, it allows agreement between discontinuous alignments. The resulting system shows substantial improvements in both alignment quality and translation quality over word-based Hidden Markov Models, while maintaining asymptotically equivalent runtime.

• Type: Journal
• Year: 2011
• Where: ACL
• Authors: Mohit Bansal, Chris Quirk, Robert C. Moore
• Keywords: ACL 2011, Alignment Model, Computational Linguistics, Hidden Markov Models, Natural Language Processing