tailieunhanh - Báo cáo khoa học: "Forest Reranking: Discriminative Parsing with Non-Local Features∗"

Conventional n-best reranking techniques often suffer from the limited scope of the nbest list, which rules out many potentially good alternatives. We instead propose forest reranking, a method that reranks a packed forest of exponentially many parses. Since exact inference is intractable with non-local features, we present an approximate algorithm inspired by forest rescoring that makes discriminative training practical over the whole Treebank. | Forest Reranking Discriminative Parsing with Non-Local Features Liang Huang University of Pennsylvania Philadelphia PA 19104 lhuang3@ Abstract Conventional n-best reranking techniques often 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 forest of exponentially many parses. Since exact inference is intractable with non-local features we present an approximate algorithm inspired by forest rescoring that makes discriminative training practical over the whole Treebank. Our final result an F-score of outperforms both 50-best and 100-best reranking baselines and is better than any previously reported systems trained on the Treebank. 1 Introduction Discriminative reranking has become a popular technique for many NLP problems in particular parsing Collins 2000 and machine translation Shen et al. 2005 . Typically this method first generates a list of top-n candidates from a baseline system and then reranks this n-best list with arbitrary features that are not computable or intractable to compute within the baseline system. But despite its apparent success there remains a major drawback this method suffers from the limited scope of the n-best list which rules out many potentially good alternatives. For example 41 of the correct parses were not in the candidates of 30-best parses in Collins 2000 . This situation becomes worse with longer sentences because the number of possible interpretations usually grows exponentially with the Part of this work was done while I was visiting Institute of Computing Technology Beijing and I thank Prof. Qun Liu and his lab for hosting me. I am also grateful to Dan Gildea and Mark Johnson for inspirations Eugene Charniak for help with his parser and Wenbin Jiang for guidance on perceptron averaging. This project was supported by NSF ITR EIA-0205456. local non-local conventional reranking DP-based discrim. .