文章基本信息

标题：Sampling Tree Fragments from Forests
本地全文：下载
作者：Tagyoung Chung ; Licheng Fang ; Daniel Gildea 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2014
卷号：40
期号：1
页码：203-229
DOI：10.1162/COLI_a_00170
语种：English
出版社：MIT Press
摘要：We study the problem of sampling trees from forests, in the setting where probabilities for each tree may be a function of arbitrarily large tree fragments. This setting extends recent work for sampling to learn Tree Substitution Grammars to the case where the tree structure (TSG derived tree) is not fixed. We develop a Markov chain Monte Carlo algorithm which corrects for the bias introduced by unbalanced forests, and we present experiments using the algorithm to learn Synchronous Context-Free Grammar rules for machine translation. In this application, the forests being sampled represent the set of Hiero-style rules that are consistent with fixed input word-level alignments. We demonstrate equivalent machine translation performance to standard techniques but with much smaller grammars.