摘要
tion is problematic for distortion-based alignment models. Alignments to NULL demand a special treatment as they would otherwise induce very long jumps that one does not usually observe in distortion-based alignment models. Examples of this can be found in Vogel et al. (1996), who drop the NULL word entirely and thus force all source words to align lexically, and Och and Ney (2003), who choose a fixed NULL probability. In the present work, we introduce a family of IBM-style alignment models that can express dependencies between translated and untranslated source words. The models do not use NULL words and instead allow untranslatable source words to be generated from translated words in their context. This is achieved by modelling source word collocations. From a technical point of view the model can be seen as a mixture of an alignment and a language model. In word alignment certain source words are only needed for fluency reasons and do not have a translation on the target side. Most word alignment models assume a target NULL word from which they generate these untranslatable source words. Hypothesising a target NULL word is not without problems, however. For example, because this NULL word has a position, it interferes with the distribution over alignment jumps. We present a word alignment model that accounts for untranslatable source words by generating them from preceding source words. It thereby removes the need for a target NULL word and only models alignments between word pairs that are actually observed in the data. Translation experiments on English paired with Czech, German, French and Japanese show that the model outperforms its traditional IBM counterparts in terms of BLEU score. 1 2 IBM models 1 and 2 Here, we quickly review the IBM alignment models 1 and 2 (Brown et al., 1993). We assume a random variable E over the English (target) vocabulary2 , a variable F over the French (source) vocabulary and a variable A over alignment links3 . The IBM models assign probabilities to alignment configurations and source sentences given the target side. Under the assumption that all source words are conditionally independent given the alignment links, these probabilities factorise as