Paper: Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features

ACL ID P14-1005
Title Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We investigate different ways of learning structured perceptron models for coref- erence resolution when using non-local features and beam search. Our experi- mental results indicate that standard tech- niques such as early updates or Learning as Search Optimization (LaSO) perform worse than a greedy baseline that only uses local features. By modifying LaSO to de- lay updates until the end of each instance we obtain significant improvements over the baseline. Our model obtains the best results to date on recent shared task data for Arabic, Chinese, and English.