Soft Syntactic Constraints for Hierarchical Phrased-Based Translation

Proceedings of ACL-08: HLT, pages 1003–1011, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Soft Syntactic Constraints for Hierarchical Phrased-Based Translation Yuval Marton and Philip Resnik Department of Linguistics and the Laboratory for Computational Linguistics and Information Processing (CLIP) at the Institute for Advanced Computer Studies (UMIACS) University of Maryland, College Park, MD 20742-7505, USA {ymarton, resnik} @t umiacs.umd.edu Abstract In adding syntax to statistical MT, there is a tradeoff between taking advantage of lin- guistic analysis, versus allowing the model to exploit linguistically unmotivated mappings learned from parallel training data. A num- ber of previous efforts have tackled this trade- off by starting with a commitment to linguisti- cally motivated analyses and then finding ap- propriate ways to soften that commitment. We present an approach that explores the trade- off from the other direction, starting with a context-free translation model learned directly from aligned parallel text, and then adding soft constituent-level constraints based on parses of the source language. We obtain substantial improvements in performance for translation from Chinese and Arabic to English. 1 Introduction The statistical revolution in machine translation, be- ginning with (Brown et al., 1993) in the early 1990s, replaced an earlier era of detailed language analy- sis with automatic learning of shallow source-target mappings from large parallel corpora. Over the last several years, however, the pendulum has begun to swing back in the other direction, with researchers exploring a variety of statistical models that take ad- vantage of source- and particularly target-language syntactic analysis (e.g. (Cowan et al., 2006; Zoll- mann and Venugopal, 2006; Marcu et al., 2006; Gal- ley et al., 2006) and numerous others). Chiang (2005) distinguishes statistical MT ap- proaches that are “syntactic” in a formal sense, go- ing beyond the finite-state underpinnings of phrase- based models, from approaches that are syntactic in a linguistic sense, i.e. taking advantage of a priori language knowledge in the form of annota- tions derived from human linguistic analysis or tree- banking.1 The two forms of syntactic modeling are doubly dissociable: current research frameworks in- clude systems that are finite state but informed by linguistic annotation prior to training (e.g., (Koehn and Hoang, 2007; Birch et al., 2007; Hassan et al., 2007)), and also include systems employing context- free models trained on parallel text without benefit of any prior linguistic analysis (e.g. (Chiang, 2005; Chiang, 2007; Wu, 1997)). Over time, however, there has been increasing movement in the direction of systems that are syntactic in both the formal and linguistic senses. In any such system, there is a natural tension be- tween taking advantage of the linguistic analysis, versus allowing the model to use linguistically un- motivated mappings learned from parallel training data. The tradeoff often involves starting with a sys- tem that exploits rich linguistic representations and relaxing some part of it. For example, DeNeefe et al. (2007) begin with a tree-to-string model, using treebank-based target language analysis, and find it useful to modify it in order to accommodate useful “phrasal” chunks that …

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