Abstract
Chinese word segmentation (CWS) is a necessary step in Chinese-English statistical machine translation (SMT) and its performance has an impact on the results of SMT. However, there are many settings involved in creating a CWS system such as various specifications and CWS methods. This paper investigates the effect of these settings to SMT. We tested dictionary-based and CRF-based approaches and found there was no significant difference between the two in the qualty of the resulting translations. We also found the correlation between the CWS F-score and SMT BLEU score was very weak. This paper also proposes two methods of combining advantages of different specifications: a simple concatenation of training data and a feature interpolation approach in which the same types of features of translation models from various CWS schemes are linearly interpolated. We found these approaches were very effective in improving quality of translations.
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