Minimum Bayes-Risk Word Alignments of Bilingual Texts

Minimum Bayes-Risk Word Alignments of Bilingual Texts Shankar Kumar and William Byrne Center for Language and Speech Processing, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA skumar,byrne @jhu.edu Abstract We present Minimum Bayes-Risk word alignment for machine translation. This statistical, model-based approach attempts to minimize the expected risk of align- ment errors under loss functions that mea- sure alignment quality. We describe var- ious loss functions, including some that incorporate linguistic analysis as can be obtained from parse trees, and show that these approaches can improve alignments of the English-French Hansards. 1 Introduction The automatic determination of word alignments in bilingual corpora would be useful for Natural Lan- guage Processing tasks such as statistical machine translation, automatic dictionary construction, and multilingual document retrieval. The development of techniques in all these areas would be facili- tated by automatic performance metrics, and align- ment and translation quality metrics have been pro- posed (Och and Ney, 2000b; Papineni et al., 2002). However, given the difficulty of judging translation quality, it is unlikely that a single, global metric will be found for any of these tasks. It is more likely that specialized metrics will be developed to mea- sure specific aspects of system performance. This is even desirable, as these specialized metrics could be used in tuning systems for particular applications. We have applied Minimum Bayes-Risk (MBR) procedures developed for automatic speech recog- nition (Goel and Byrne, 2000) to word alignment of bitexts. This is a modeling approach that can be used with statistical models of speech and language to de- velop algorithms that are optimized for specific loss functions. We will discuss loss functions that can be used for word alignment and show how the over- all alignment process can be improved by the use of loss functions that incorporate linguistic features, such as parses and part-of-speech tags. 2 Word-to-Word Bitext Alignment We will study the problem of aligning an English sentence to a French sentence and we will use the word alignment of the IBM statistical translation models (Brown et al., 1993). Let and denote a pair of translated English and French sentences. An English word is defined as an ordered pair , where the index refers to the posi- tion of the word in the English sentence; vocabulary of English; and the word at positionis the the NULL word to which “spurious” French wordsis may be aligned. Similarly, a French word is written as . An alignment between and is defined to be a sequence where . Under the alignment , the French word is connected to the English word . For every alignment , we define a link set defined as whose ele- ments are given by the alignment links . 3 Alignment Loss Functions In this section we introduce loss functions to mea- sure the quality of automatically produced align- ments. Suppose we wish to compare an automat- ically produced alignment to a reference align- ment , which we assume was produced by a com- petent …

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