Symmetric Word Alignments for Statistical Machine Translation

Symmetric Word Alignments for Statistical Machine Translation Evgeny Matusov and Richard Zens and Hermann Ney Lehrstuhl f¨ur Informatik VI, Computer Science Department RWTH Aachen University D-52056 Aachen, Germany {matusov,zens,ney}@cs.rwth-aachen.de Abstract In this paper, we address the word alignment problem for statistical machine translation. We aim at creating a sym- metric word alignment allowing for reli- able one-to-many and many-to-one word relationships. We perform the iterative alignment training in the source-to-target and the target-to-source direction with the well-known IBM and HMM alignment models. Using these models, we robustly estimate the local costs of aligning a source word and a target word in each sentence pair. Then, we use efficient graph algo- rithms to determine the symmetric align- ment with minimal total costs (i. e. max- imal alignment probability). We evalu- ate the automatic alignments created in this way on the German–English Verb- mobil task and the French–English Cana- dian Hansards task. We show statistically significant improvements of the alignment quality compared to the best results re- ported so far. On the Verbmobil task, we achieve an improvement of more than 1% absolute over the baseline error rate of 4.7%. 1 Introduction Word-aligned bilingual corpora provide im- portant knowledge for many natural language processing tasks, such as the extraction of bilingual word or phrase lexica (Melamed, 2000; Och and Ney, 2000). The solutions of these problems depend heavily on the quality of the word alignment (Och and Ney, 2000). Word alignment models were first introduced in statistical machine translation (Brown et al., 1993). An alignment describes a mapping from source sentence words to target sentence words. Using the IBM translation models IBM-1 to IBM-5 (Brown et al., 1993), as well as the Hidden-Markov alignment model (Vogel et al., 1996), we can produce alignments of good quality. However, all these models constrain the alignments so that a source word can be aligned to at most one target word. This con- straint is useful to reduce the computational complexity of the model training, but makes it hard to align phrases in the target lan- guage (English) such as ‘the day after tomor- row’ to one word in the source language (Ger- man) ‘¨ubermorgen’. We will present a word alignment algorithm which avoids this con- straint and produces symmetric word align- ments. This algorithm considers the align- ment problem as a task of finding the edge cover with minimal costs in a bipartite graph. The parameters of the IBM models and HMM, in particular the state occupation probabili- ties, will be used to determine the costs of aligning a specific source word to a target word. We will evaluate the suggested alignment methods on the German–English Verbmo- bil task and the French–English Canadian Hansards task. We will show statistically sig- nificant improvements compared to state-of- the-art results in (Och and Ney, 2003). 2 Statistical Word Alignment Models In this section, we will give an overview of the commonly used statistical word alignment techniques. They are based on the source- channel approach to statistical machine trans- …

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