Pseudo-Word for Phrase-Based Machine Translation

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 148–156, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Pseudo-word for Phrase-based Machine Translation Xiangyu Duan Min Zhang Haizhou Li Institute for Infocomm Research, A-STAR, Singapore {Xduan, mzhang, hli}@i2r.a-star.edu.sg Abstract The pipeline of most Phrase-Based Statistical Machine Translation (PB-SMT) systems starts from automatically word aligned parallel cor- pus. But word appears to be too fine-grained in some cases such as non-compositional phrasal equivalences, where no clear word alignments exist. Using words as inputs to PB- SMT pipeline has inborn deficiency. This pa- per proposes pseudo-word as a new start point for PB-SMT pipeline. Pseudo-word is a kind of basic multi-word expression that character- izes minimal sequence of consecutive words in sense of translation. By casting pseudo-word searching problem into a parsing framework, we search for pseudo-words in a monolingual way and a bilingual synchronous way. Ex- periments show that pseudo-word significantly outperforms word for PB-SMT model in both travel translation domain and news translation domain. 1 Introduction The pipeline of most Phrase-Based Statistical Machine Translation (PB-SMT) systems starts from automatically word aligned parallel corpus generated from word-based models (Brown et al., 1993), proceeds with step of induction of phrase table (Koehn et al., 2003) or synchronous gram- mar (Chiang, 2007) and with model weights tun- ing step. Words are taken as inputs to PB-SMT at the very beginning of the pipeline. But there is a deficiency in such manner that word is too fine- grained in some cases such as non-compositional phrasal equivalences, where clear word align- ments do not exist. For example in Chinese-to- English translation, “想” and “would like to” constitute a 1-to-n phrasal equivalence, “多少 钱” and “how much is it” constitute a m-to-n phrasal equivalence. No clear word alignments are there in such phrasal equivalences. Moreover, should basic translational unit be word or coarse- grained multi-word is an open problem for opti- mizing SMT models. Some researchers have explored coarse- grained translational unit for machine translation. Marcu and Wong (2002) attempted to directly learn phrasal alignments instead of word align- ments. But computational complexity is prohibi- tively high for the exponentially large number of decompositions of a sentence pair into phrase pairs. Cherry and Lin (2007) and Zhang et al. (2008) used synchronous ITG (Wu, 1997) and constraints to find non-compositional phrasal equivalences, but they suffered from intractable estimation problem. Blunsom et al. (2008; 2009) induced phrasal synchronous grammar, which aimed at finding hierarchical phrasal equiva- lences. Another direction of questioning word as basic translational unit is to directly question word segmentation on languages where word bounda- ries are not orthographically marked. In Chinese- to-English translation task where Chinese word boundaries are not marked, Xu et al. (2004) used word aligner to build a Chinese dictionary to re- segment Chinese sentence. Xu et al. (2008) used a Bayesian semi-supervised method that com- bines Chinese word segmentation model and Chinese-to-English translation model to derive a Chinese segmentation suitable for machine trans- lation. …

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