Paper: Relaxed Marginal Inference and its Application to Dependency Parsing

ACL ID N10-1117
Title Relaxed Marginal Inference and its Application to Dependency Parsing
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

Recently, relaxation approaches have been successfully used for MAP inference on NLP problems. Inthisworkweshowhowtoextend the relaxation approach to marginal inference used in conditional likelihood training, pos- terior decoding, confidence estimation, and other tasks. We evaluate our approach for the case of second-order dependency parsing and observe a tenfold increase in parsing speed, with no loss in accuracy, by performing in- ference over a small subset of the full factor graph. We also contribute a bound on the error of the marginal probabilities by a sub-graph with respect to the full graph. Finally, while only evaluated with BP in this paper, our ap- proach is general enough to be applied with any marginal inference method in the inner loop.