Tailoring Word Alignments to Syntactic Machine Translation

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 17–24, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Tailoring Word Alignments to Syntactic Machine Translation John DeNero Computer Science Division University of California, Berkeley denero@berkeley.edu Dan Klein Computer Science Division University of California, Berkeley klein@cs.berkeley.edu Abstract Extracting tree transducer rules for syntac- tic MT systems can be hindered by word alignment errors that violate syntactic corre- spondences. We propose a novel model for unsupervised word alignment which explic- itly takes into account target language con- stituent structure, while retaining the robust- ness and efficiency of the HMM alignment model. Our model’s predictions improve the yield of a tree transducer extraction system, without sacrificing alignment quality. We also discuss the impact of various posterior- based methods of reconciling bidirectional alignments. 1 Introduction Syntactic methods are an increasingly promising ap- proach to statistical machine translation, being both algorithmically appealing (Melamed, 2004; Wu, 1997) and empirically successful (Chiang, 2005; Galley et al., 2006). However, despite recent progress, almost all syntactic MT systems, indeed statistical MT systems in general, build upon crude legacy models of word alignment. This dependence runs deep; for example, Galley et al. (2006) requires word alignments to project trees from the target lan- guage to the source, while Chiang (2005) requires alignments to induce grammar rules. Word alignment models have not stood still in re- cent years. Unsupervised methods have seen sub- stantial reductions in alignment error (Liang et al., 2006) as measured by the now much-maligned AER metric. A host of discriminative methods have been introduced (Taskar et al., 2005; Moore, 2005; Ayan and Dorr, 2006). However, few of these methods have explicitly addressed the tension between word alignments and the syntactic processes that employ them (Cherry and Lin, 2006; Daum´e III and Marcu, 2005; Lopez and Resnik, 2005). We are particularly motivated by systems like the one described in Galley et al. (2006), which con- structs translations using tree-to-string transducer rules. These rules are extracted from a bitext anno- tated with both English (target side) parses and word alignments. Rules are extracted from target side constituents that can be projected onto contiguous spans of the source sentence via the word alignment. Constituents that project onto non-contiguous spans of the source sentence do not yield transducer rules themselves, and can only be incorporated into larger transducer rules. Thus, if the word alignment of a sentence pair does not respect the constituent struc- ture of the target sentence, then the minimal transla- tion units must span large tree fragments, which do not generalize well. We present and evaluate an unsupervised word alignment model similar in character and compu- tation to the HMM model (Ney and Vogel, 1996), but which incorporates a novel, syntax-aware distor- tion component which conditions on target language parse trees. These trees, while automatically gener- ated and therefore imperfect, are nonetheless (1) a useful source of structural bias and (2) the same trees which constrain future stages of processing anyway. In …

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