Word alignment with Stochastic Bracketing Linear Inversion Transduction Grammar

Authors:
Markus Saers;Joakim Nivre;Dekai Wu
Affiliations:
Uppsala University, Sweden;Uppsala University, Sweden;HKUST, Hong Kong
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

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Abstract

The class of Linear Inversion Transduction Grammars (litgs) is introduced, and used to induce a word alignment over a parallel corpus. We show that alignment via Stochastic Bracketing litgs is considerably faster than Stochastic Bracketing itgs, while still yielding alignments superior to the widely-used heuristic of intersecting bidirectional ibm alignments. Performance is measured as the translation quality of a phrase-based machine translation system built upon the word alignments, and an improvementof 2.85 bleu points over baseline is noted for French--English.