Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph
This work addresses the challenge of accurately classifying implicit discourse relations in natural language processing, which is incremental as it builds on existing methods by incorporating paragraph-level context.
The paper tackled the problem of implicit discourse relation classification by modeling inter-dependencies between discourse units at the paragraph level, resulting in a model that outperforms previous state-of-the-art systems on the PDTB benchmark corpus.
We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.