Aspect Marker Generation in English-to-Chinese Machine Translation

This paper reports on a pilot study of aspect marker generation in an English-to-Chinese translation scenario. Our classifier combines a number of linguistic features in a Maximum Entropy frame-work and achieves an overall accuracy of 78%. We also investigate the impact of different clusters of linguistic features; we find that syntactic features have the highest utility and lexical aspectual prop-erties associated with verbs do not have signifi-cant contribution to the prediction of aspect mark-ers. Furthermore, we have demonstrated converg-ing evidence that there is only marginal sequential dependency between the aspect markers of differ-ent verbs in the same sentence.