Joint Decoding with Multiple Translation Models

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 576–584, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Joint Decoding with Multiple Translation Models Yang Liu and Haitao Mi and Yang Feng and Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704, Beijing 100190, China {yliu,htmi,fengyang,liuqun}@ict.ac.cn Abstract Current SMT systems usually decode with single translation models and cannot ben- efit from the strengths of other models in decoding phase. We instead propose joint decoding, a method that combines multi- ple translation models in one decoder. Our joint decoder draws connections among multiple models by integrating the trans- lation hypergraphs they produce individu- ally. Therefore, one model can share trans- lations and even derivations with other models. Comparable to the state-of-the-art system combination technique, joint de- coding achieves an absolute improvement of 1.5 BLEU points over individual decod- ing. 1 Introduction System combination aims to find consensus trans- lations among different machine translation sys- tems. It proves that such consensus translations are usually better than the output of individual sys- tems (Frederking and Nirenburg, 1994). Recent several years have witnessed the rapid development of system combination methods based on confusion networks (e.g., (Rosti et al., 2007; He et al., 2008)), which show state-of-the- art performance in MT benchmarks. A confusion network consists of a sequence of sets of candidate words. Each candidate word is associated with a score. The optimal consensus translation can be obtained by selecting one word from each set of candidates to maximizing the overall score. While it is easy and efficient to manipulate strings, cur- rent methods usually have no access to most infor- mation available in decoding phase, which might be useful for obtaining further improvements. In this paper, we propose a framework for com- bining multiple translation models directly in de- coding phase. 1 Based on max-translation decod- ing and max-derivation decoding used in conven- tional individual decoders (Section 2), we go fur- ther to develop a joint decoder that integrates mul- tiple models on a firm basis: • Structuring the search space of each model as a translation hypergraph (Section 3.1), our joint decoder packs individual translation hypergraphs together by merging nodes that have identical partial translations (Section 3.2). Although such translation-level combi- nation will not produce new translations, it does change the way of selecting promising candidates. • Two models could even share derivations with each other if they produce the same structures on the target side (Section 3.3), which we refer to as derivation-level com- bination. This method enlarges the search space by allowing for mixing different types of translation rules within one derivation. • As multiple derivations are used for finding optimal translations, we extend the minimum error rate training (MERT) algorithm (Och, 2003) to tune feature weights with respect to BLEU score for max-translation decoding (Section 4). We evaluated our joint decoder that integrated a hierarchical phrase-based model (Chiang, 2005; Chiang, …

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