Topological Ordering of Function Words in Hierarchical Phrase-based Translation

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 324–332, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Topological Ordering of Function Words in Hierarchical Phrase-based Translation Hendra Setiawan1 and Min-Yen Kan2 and Haizhou Li3 and Philip Resnik1 1University of Maryland Institute for Advanced Computer Studies 2School of Computing, National University of Singapore 3Human Language Technology, Institute for Infocomm Research, Singapore {hendra,resnik}@umiacs.umd.edu, kanmy@comp.nus.edu.sg, hli@i2r.a-star.edu.sg Abstract Hierarchical phrase-based models are at- tractive because they provide a consis- tent framework within which to character- ize both local and long-distance reorder- ings, but they also make it dif�cult to distinguish many implausible reorderings from those that are linguistically plausi- ble. Rather than appealing to annotation- driven syntactic modeling, we address this problem by observing the in�uential role of function words in determining syntac- tic structure, and introducing soft con- straints on function word relationships as part of a standard log-linear hierarchi- cal phrase-based model. Experimentation on Chinese-English and Arabic-English translation demonstrates that the approach yields signi�cant gains in performance. 1 Introduction Hierarchical phrase-based models (Chiang, 2005; Chiang, 2007) offer a number of attractive bene- �ts in statistical machine translation (SMT), while maintaining the strengths of phrase-based systems (Koehn et al., 2003). The most important of these is the ability to model long-distance reordering ef- �ciently. To model such a reordering, a hierar- chical phrase-based system demands no additional parameters, since long and short distance reorder- ings are modeled identically using synchronous context free grammar (SCFG) rules. The same rule, depending on its topological ordering � i.e. its position in the hierarchical structure � can af- fect both short and long spans of text. Interest- ingly, hierarchical phrase-based models provide this bene�t without making any linguistic commit- ments beyond the structure of the model. However, the system's lack of linguistic com- mitment is also responsible for one of its great- est drawbacks. In the absence of linguistic knowl- edge, the system models linguistic structure using an SCFG that contains only one type of nontermi- nal symbol1. As a result, the system is susceptible to the overgeneration problem: the grammar may suggest more reordering choices than appropriate, and many of those choices lead to ungrammatical translations. Chiang (2005) hypothesized that incorrect re- ordering choices would often correspond to hier- archical phrases that violate syntactic boundaries in the source language, and he explored the use of a �constituent feature� intended to reward the application of hierarchical phrases which respect source language syntactic categories. Although this did not yield signi�cant improvements, Mar- ton and Resnik (2008) and Chiang et al. (2008) extended this approach by introducing soft syn- tactic constraints similar to the constituent feature, but more �ne-grained and sensitive to distinctions among syntactic categories; these led to substan- tial improvements in performance. Zollman et al. (2006) took a complementary approach, constrain- ing the application of hierarchical rules to respect syntactic boundaries in the target language syn- tax. Whether the focus is on constraints from the source language …

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