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 anal-ysis. In this paper, we focus on label-ing full argument spans of discourse con-nectives 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 argu-ment labeling, which integrates the ad-vantages of both linear tagging and sub-tree extraction. In particular, the pro-posed approach unifies intra- and inter-sentence cases by treating the immediate-ly preceding sentence as a special con-stituent. Besides, a joint inference mech-anism is introduced to incorporate glob-al information across arguments into our constituent-based approach via integer lin-ear programming. Evaluation on PDT-B shows significant performance improve-ments of our constituent-based approach over the best state-of-the-art system. It al-so shows the effectiveness of our joint in-ference mechanism in modeling global in-formation across arguments.