Unifying Discourse Resources with Dependency Framework

TL;DR

This paper proposes a unified discourse dependency framework to integrate various Chinese discourse corpora, enabling semi-automatic conversion and improving parsing performance through leveraging combined data.

Contribution

It introduces a novel framework unifying multiple discourse annotation schemes and demonstrates how to enhance discourse parsing with this unified data.

Findings

Successful unification of diverse discourse corpora

Improved parser performance using combined data

Effective semi-automatic conversion methods

Abstract

For text-level discourse analysis, there are various discourse schemes but relatively few labeled data, because discourse research is still immature and it is labor-intensive to annotate the inner logic of a text. In this paper, we attempt to unify multiple Chinese discourse corpora under different annotation schemes with discourse dependency framework by designing semi-automatic methods to convert them into dependency structures. We also implement several benchmark dependency parsers and research on how they can leverage the unified data to improve performance.