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Báo cáo khoa học: "Model-Based Aligner Combination Using Dual Decomposition"

Unsupervised word alignment is most often modeled as a Markov process that generates a sentence f conditioned on its translation e. A similar model generating e from f will make different alignment predictions. Statistical machine translation systems combine the predictions of two directional models, typically using heuristic combination procedures like grow-diag-final. This paper presents a graphical model that embeds two directional aligners into a single model. | Model-Based Aligner Combination Using Dual Decomposition John DeNero Google Research denero@google.com Klaus Macherey Google Research kmach@google.com Abstract Unsupervised word alignment is most often modeled as a Markov process that generates a sentence f conditioned on its translation e. A similar model generating e from f will make different alignment predictions. Statistical machine translation systems combine the predictions of two directional models typically using heuristic combination procedures like grow-diag-final. This paper presents a graphical model that embeds two directional aligners into a single model. Inference can be performed via dual decomposition which reuses the efficient inference algorithms of the directional models. Our bidirectional model enforces a one-to-one phrase constraint while accounting for the uncertainty in the underlying directional models. The resulting alignments improve upon baseline combination heuristics in word-level and phrase-level evaluations. 1 Introduction Word alignment is the task of identifying corresponding words in sentence pairs. The standard approach to word alignment employs directional Markov models that align the words of a sentence f to those of its translation e such as IBM Model 4 Brown et al. 1993 or the HMM-based alignment model Vogel et al. 1996 . Machine translation systems typically combine the predictions of two directional models one which aligns f to e and the other e to f Och et al. 1999 . Combination can reduce errors and relax the one-to-many structural restriction of directional models. Common combination methods include the union or intersection of directional alignments as 420 well as heuristic interpolations between the union and intersection like grow-diag-final Koehn et al. 2003 . This paper presents a model-based alternative to aligner combination. Inference in a probabilistic model resolves the conflicting predictions of two directional models while taking into account each model s .