Paper: Fast Inference in Phrase Extraction Models with Belief Propagation

ACL ID N12-1004
Title Fast Inference in Phrase Extraction Models with Belief Propagation
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012
Authors

Modeling overlapping phrases in an align- ment model can improve alignment quality but comes with a high inference cost. For example, the model of DeNero and Klein (2010) uses an ITG constraint and beam-based Viterbi decoding for tractability, but is still slow. We first show that their model can be approximated using structured belief propaga- tion, with a gain in alignment quality stem- ming from the use of marginals in decoding. We then consider a more flexible, non-ITG matching constraint which is less efficient for exact inference but more efficient for BP. With this new constraint, we achieve a relative error reduction of 40% in F5 and a 5.5x speed-up.