tailieunhanh - Báo cáo khoa học: "Discriminative Pruning for Discriminative ITG Alignment"

While Inversion Transduction Grammar (ITG) has regained more and more attention in recent years, it still suffers from the major obstacle of speed. We propose a discriminative ITG pruning framework using Minimum Error Rate Training and various features from previous work on ITG alignment. Experiment results show that it is superior to all existing heuristics in ITG pruning. On top of the pruning framework, we also propose a discriminative ITG alignment model using hierarchical phrase pairs, which improves both F-score and Bleu score over the baseline alignment system of GIZA++. . | Discriminative Pruning for Discriminative ITG Alignment Shujie Liu Chi-Ho Li and Ming Zhou School of Computer Science and Technology Harbin Institute of Technology Harbin China shuj ieliu@mtl Microsoft Research Asia Beijing China chl mingzhou @ Abstract While Inversion Transduction Grammar ITG has regained more and more attention in recent years it still suffers from the major obstacle of speed. We propose a discriminative ITG pruning framework using Minimum Error Rate Training and various features from previous work on ITG alignment. Experiment results show that it is superior to all existing heuristics in ITG pruning. On top of the pruning framework we also propose a discriminative ITG alignment model using hierarchical phrase pairs which improves both F-score and Bleu score over the baseline alignment system of GIZA . 1 Introduction Inversion transduction grammar ITG Wu 1997 is an adaptation of SCFG to bilingual parsing. It does synchronous parsing of two languages with phrasal and word-level alignment as by-product. For this reason ITG has gained more and more attention recently in the word alignment community Zhang and Gildea 2005 Cherry and Lin 2006 Haghighi etal. 2009 . A major obstacle in ITG alignment is speed. The original unsupervised ITG algorithm has complexity of O n6 . When extended to super-vised discriminative framework ITG runs even more slowly. Therefore all attempts to ITG alignment come with some pruning method. For example Haghighi et al. 2009 do pruning based on the probabilities of links from a simpler alignment model viz. HMM Zhang and Gildea 2005 propose Tic-tac-toe pruning which is based on the Model 1 probabilities of word pairs inside and outside a pair of spans. As all the principles behind these techniques have certain contribution in making good pruning decision it is tempting to incorporate all these features in ITG pruning. In this paper we pro pose a novel discriminative pruning framework for .