Dealing with spurious ambiguity in learning ITG-based word alignment

Shujian Huang, Stephan Vogel, Jiajun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Word alignment has an exponentially large search space, which often makes exact inference infeasible. Recent studies have shown that inversion transduction grammars are reasonable constraints for word alignment, and that the constrained space could be efficiently searched using synchronous parsing algorithms. However, spurious ambiguity may occur in synchronous parsing and cause problems in both search efficiency and accuracy. In this paper, we conduct a detailed study of the causes of spurious ambiguity and how it effects parsing and discriminative learning. We also propose a variant of the grammar which eliminates those ambiguities. Our grammar shows advantages over previous grammars in both synthetic and real-world experiments.

Original languageEnglish
Title of host publicationACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Pages379-383
Number of pages5
Volume2
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 - Portland, OR, United States
Duration: 19 Jun 201124 Jun 2011

Other

Other49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
CountryUnited States
CityPortland, OR
Period19/6/1124/6/11

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Huang, S., Vogel, S., & Chen, J. (2011). Dealing with spurious ambiguity in learning ITG-based word alignment. In ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Vol. 2, pp. 379-383)