Paper: Forest Reranking: Discriminative Parsing with Non-Local Features

ACL ID P08-1067
Title Forest Reranking: Discriminative Parsing with Non-Local Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors
  • Liang Huang (University of Pennsylvania, Philadelphia PA)

Conventional n-best reranking techniques of- ten suffer from the limited scope of the n- best list, which rules out many potentially good alternatives. We instead propose forest reranking, a method that reranks a packed for- est of exponentially many parses. Since ex- act inference is intractable with non-local fea- tures, we present an approximate algorithm in- spired by forest rescoring that makes discrim- inative training practical over the whole Tree- bank. Our final result, an F-score of 91.7, out- performs both 50-best and 100-best reranking baselines, and is better than any previously re- ported systems trained on the Treebank.