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Description

Lexical selection is crucial for statistical machine translation. Previous studies separately exploit sentence-level contexts and documentlevel topics for lexical selection, neglecting their correlations. In this paper, we propose a context-aware topic model for lexical selection, which not only models local contexts and global topics but also captures their correlations. The model uses target-side translations as hidden variables to connect document topics and source-side local contextual words. In order to learn hidden variables and distributions from data, we introduce a Gibbs sampling algorithm for statistical estimation and inference. A new translation probability based on distributions learned by the model is integrated into a translation system for lexical selection. Experiment results on NIST Chinese-English test sets demonstrate that 1) our model significantly outperforms previous lexical selection methods and 2) modeling correlations between local words and global topics can further improve translation quality.

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