A Constituent-Based Approach to Argument Labeling with Joint Inference in Discourse Parsing

Discourse parsing is a challenging task and plays a critical role in discourse analysis. In this paper, we focus on labeling full argument spans of discourse connectives in the Penn Discourse Treebank (PDTB). Previous studies cast this task as a linear tagging or subtree extraction problem. In this paper, we propose a novel constituent-based approach to argument labeling, which integrates the advantages of both linear tagging and subtree extraction. In particular, the proposed approach unifies intraand intersentence cases by treating the immediately preceding sentence as a special constituent. Besides, a joint inference mechanism is introduced to incorporate global information across arguments into our constituent-based approach via integer linear programming. Evaluation on PDTB shows significant performance improvements of our constituent-based approach over the best state-of-the-art system. It also shows the effectiveness of our joint inference mechanism in modeling global information across arguments.