Easy-First Chinese POS Tagging and Dependency Parsing

Abstract

The easy-first non-directional dependency parser has demonstrated its advantage over transition based dependency parsers which parse sentences from left to right. This work investigates easy-first method on Chinese POS tagging, dependency parsing and joint tagging and dependency parsing. In particular, we generalize the easy-first dependency parsing algorithm to a general framework and apply this framework to Chinese POS tagging and dependency parsing. We then propose the first joint tagging and dependency parsing algorithm under the easy-first framework. We train the joint model with both supervised objective and additional loss which only relates to one of the individual tasks (either tagging or parsing). In this way, we can bias the joint model towards the preferred task. Experimental results show that both the tagger and the parser achieve state-of-the-art accuracy and runs fast. And our joint model achieves tagging accuracy of 94.27 which is the best result reported so far.