Word sense disambiguation: a survey <s> Machine translation (MT): <s> We directly investigate a subject of much recent debate: do word sense disambiguation models help statistical machine translation quality? We present empirical results casting doubt on this common, but unproved, assumption. Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. Error analysis suggests several key factors behind this surprising finding, including inherent limitations of current statistical MT architectures. <s> BIB001 </s> Word sense disambiguation: a survey <s> Machine translation (MT): <s> Recent research presents conflicting evidence on whether word sense disambiguation (WSD) systems can help to improve the performance of statistical machine translation (MT) systems. In this paper, we successfully integrate a state-of-the-art WSD system into a state-of-the-art hierarchical phrase-based MT system, Hiero. We show for the first time that integrating a WSD system improves the performance of a state-ofthe-art statistical MT system on an actual translation task. Furthermore, the improvement is statistically significant. <s> BIB002
WSD is required for MT BIB001 [15] [16] BIB002 , as a few words in every language have different translations based on the contexts of their use. For example, in the English sentences, "He scored a goal", "It was his goal in life"-the word "goal" carries different meanings which is a big issue during language translation.