Improving Chinese Semantic Role Labeling with English Proposition Bank

摘要

Most researches to SRL focus on English. It is still a challenge to improve the SRL performance of other language. In this paper, we introduce a two-pass approach to do Chinese SRL with a Recurrent Neural Network (RNN) model. We use English Proposition Bank (EPB) to improve the performance of Chinese SRL. Experimental result shows a significant improvement over the state-of-the-art methods on Chinese Proposition Bank (CPB), which reaches 78.39% F1 score.