A Syntax-Driven Bracketing Model for Phrase-Based Translation

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 315–323, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP A Syntax-Driven Bracketing Model for Phrase-Based Translation Deyi Xiong, Min Zhang, Aiti Aw and Haizhou Li Human Language Technology Institute for Infocomm Research 1 Fusionopolis Way, #21-01 South Connexis, Singapore 138632 {dyxiong, mzhang, aaiti, hli}@i2r.a-star.edu.sg Abstract Syntactic analysis influences the way in which the source sentence is translated. Previous efforts add syntactic constraints to phrase-based translation by directly rewarding/punishing a hypothesis when- ever it matches/violates source-side con- stituents. We present a new model that automatically learns syntactic constraints, including but not limited to constituent matching/violation, from training corpus. The model brackets a source phrase as to whether it satisfies the learnt syntac- tic constraints. The bracketed phrases are then translated as a whole unit by the de- coder. Experimental results and analy- sis show that the new model outperforms other previous methods and achieves a substantial improvement over the baseline which is not syntactically informed. 1 Introduction The phrase-based approach is widely adopted in statistical machine translation (SMT). It segments a source sentence into a sequence of phrases, then translates and reorder these phrases in the target. In such a process, original phrase-based decod- ing (Koehn et al., 2003) does not take advan- tage of any linguistic analysis, which, however, is broadly used in rule-based approaches. Since it is not linguistically motivated, original phrase- based decoding might produce ungrammatical or even wrong translations. Consider the following Chinese fragment with its parse tree: Src: [把 [[7月 11日]NP [设立 [为 [航海 节]NP ]PP ]VP ]IP ]VP Ref: established July 11 as Sailing Festival day Output: [to/把 [⟨[set up/设 立 [for/为 naviga- tion/航海]] on July 11/7月11日⟩ knots/节]] The output is generated from a phrase-based sys- tem which does not involve any syntactic analy- sis. Here we use “[]” (straight orientation) and “⟨⟩” (inverted orientation) to denote the common structure of the source fragment and its transla- tion found by the decoder. We can observe that the decoder inadequately breaks up the second NP phrase and translates the two words “航海” and “节” separately. However, the parse tree of the source fragment constrains the phrase “航海 节” to be translated as a unit. Without considering syntactic constraints from the parse tree, the decoder makes wrong decisions not only on phrase movement but also on the lex- ical selection for the multi-meaning word “节”1. To avert such errors, the decoder can fully respect linguistic structures by only allowing syntactic constituent translations and reorderings. This, un- fortunately, significantly jeopardizes performance (Koehn et al., 2003; Xiong et al., 2008) because by integrating syntactic constraint into decoding as a hard constraint, it simply prohibits any other use- ful non-syntactic translations which violate con- stituent boundaries. To better leverage syntactic constraint yet still allow non-syntactic translations, Chiang (2005) introduces a count for each hypothesis and ac- cumulates it whenever the hypothesis exactly matches syntactic boundaries on the source side. On the contrary, …

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