Abstract

Most current statistical machine translation (SMT) systems make very little use of contextual information to select a translation candidate for a given input language phrase. However, despite evidence that rich context features are useful in stand-alone translation disambiguation tasks, recent studies reported that incorporating context-rich approaches from Word Sense Disambiguation (WSD) methods directly into classic word-based SMT systems, surprisingly, did not yield the expected improvements in translation quality. We argue here that, instead, it is necessary to design a contextdependent lexicon that is specifically matched to a given phrase-based SMT model, rather than simply incorporating an independently built and tested WSD module. In this approach, the baseline SMT phrasal lexicon, which uses translation probabilities that are independent of context, is augmented with a context-dependent score, defined using insights from standalone translation disambiguation evaluations. This approach reliably improves performance on both IWSLT and NIST Chinese-English test sets, producing consistent gains on all eight of the most commonly used automated evaluation metrics. We analyze the behavior of the model along a number of dimensons, including an analysis confirming that the most important context features are not available in conventional phrase-based SMT models. 1

Citations