Bootstrapping Word Alignment via Word Packing

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 304–311, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Bootstrapping Word Alignment via Word Packing Yanjun Ma, Nicolas Stroppa, Andy Way School of Computing Dublin City University Glasnevin, Dublin 9, Ireland {yma,nstroppa,away}@computing.dcu.ie Abstract We introduce a simple method to pack words for statistical word alignment. Our goal is to simplify the task of automatic word align- ment by packing several consecutive words together when we believe they correspond to a single word in the opposite language. This is done using the word aligner itself, i.e. by bootstrapping on its output. We evaluate the performance of our approach on a Chinese-to-English machine translation task, and report a 12.2% relative increase in BLEU score over a state-of-the art phrase- based SMT system. 1 Introduction Automatic word alignment can be defined as the problem of determining a translational correspon- dence at word level given a parallel corpus of aligned sentences. Most current statistical models (Brown et al., 1993; Vogel et al., 1996; Deng and Byrne, 2005) treat the aligned sentences in the corpus as se- quences of tokens that are meant to be words; the goal of the alignment process is to find links be- tween source and target words. Before applying such aligners, we thus need to segment the sentences into words – a task which can be quite hard for lan- guages such as Chinese for which word boundaries are not orthographically marked. More importantly, however, this segmentation is often performed in a monolingual context, which makes the word align- ment task more difficult since different languages may realize the same concept using varying num- bers of words (see e.g. (Wu, 1997)). Moreover, a segmentation considered to be “good” from a mono- lingual point of view may be unadapted for training alignment models. Although some statistical alignment models al- low for 1-to-n word alignments for those reasons, they rarely question the monolingual tokenization and the basic unit of the alignment process remains the word. In this paper, we focus on 1-to-n align- ments with the goal of simplifying the task of auto- matic word aligners by packing several consecutive words together when we believe they correspond to a single word in the opposite language; by identifying enough such cases, we reduce the number of 1-to-n alignments, thus making the task of word alignment both easier and more natural. Our approach consists of using the output from an existing statistical word aligner to obtain a set of candidates for word packing. We evaluate the re- liability of these candidates, using simple metrics based on co-occurence frequencies, similar to those used in associative approaches to word alignment (Kitamura and Matsumoto, 1996; Melamed, 2000; Tiedemann, 2003). We then modify the segmenta- tion of the sentences in the parallel corpus accord- ing to this packing of words; these modified sen- tences are then given back to the word aligner, which produces new alignments. We evaluate the validity of our approach …

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