Forest-to-String Statistical Translation Rules

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 704–711, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Forest-to-String Statistical Translation Rules Yang Liu , Yun Huang , Qun Liu and Shouxun Lin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704, Beijing 100080, China {yliu,huangyun,liuqun,sxlin}@ict.ac.cn Abstract In this paper, we propose forest-to-string rules to enhance the expressive power of tree-to-string translation models. A forest- to-string rule is capable of capturing non- syntactic phrase pairs by describing the cor- respondence between multiple parse trees and one string. To integrate these rules into tree-to-string translation models, auxil- iary rules are introduced to provide a gen- eralization level. Experimental results show that, on the NIST 2005 Chinese-English test set, the tree-to-string model augmented with forest-to-string rules achieves a relative im- provement of 4.3% in terms of BLEU score over the original model which allows tree- to-string rules only. 1 Introduction The past two years have witnessed the rapid de- velopment of linguistically syntax-based translation models (Quirk et al., 2005; Galley et al., 2006; Marcu et al., 2006; Liu et al., 2006), which induce tree-to-string translation rules from parallel texts with linguistic annotations. They demonstrated very promising results when compared with the state of the art phrase-based system (Och and Ney, 2004) in the NIST 2006 machine translation evaluation 1. While Galley et al. (2006) and Marcu et al. (2006) put emphasis on target language analysis, Quirk et al. (2005) and Liu et al. (2006) show benefits from modeling the syntax of source language. 1See http://www.nist.gov/speech/tests/mt/ One major problem with linguistically syntax- based models, however, is that tree-to-string rules fail to syntactify non-syntactic phrase pairs because they require a syntax tree fragment over the phrase to be syntactified. Here, we distinguish between syn- tactic and non-syntactic phrase pairs. By “syntactic” we mean that the phrase pair is subsumed by some syntax tree fragment. The phrase pairs without trees over them are non-syntactic. Marcu et al. (2006) report that approximately 28% of bilingual phrases are non-syntactic on their English-Chinese corpus. We believe that it is important to make available to syntax-based models all the bilingual phrases that are typically available to phrase-based models. On one hand, phrases have been proven to be a simple and powerful mechanism for machine translation. They excel at capturing translations of short idioms, providing local re-ordering decisions, and incorpo- rating context information straightforwardly. Chi- ang (2005) shows significant improvement by keep- ing the strengths of phrases while incorporating syn- tax into statistical translation. On the other hand, the performance of linguistically syntax-based mod- els can be hindered by making use of only syntac- tic phrase pairs. Studies reveal that linguistically syntax-based models are sensitive to syntactic anal- ysis (Quirk and Corston-Oliver, 2006), which is still not reliable enough to handle real-world texts due to limited size and domain of training data. Various solutions are proposed to tackle the prob- lem. Galley et al. (2004) handle …

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