D-REX: Dialogue Relation Extraction with Explanations

Anonymous

Keywords: dialogue, relation extraction, explanation

TL;DR: We frame relation extraction as a re-ranking problem, and include relation- and entity-specific explanations as an intermediate step in the process, allowing for explainability and improving relation extraction performance.

Abstract: Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only partially labeled explanations. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that optimizes for explanation quality and relation extraction simultaneously. We frame relation extraction as a re-ranking task and include relation- and entity-specific explanations as an intermediate step of the inference process. We find that human annotators are 4.2 times more likely to prefer D-REX's explanations over a joint relation extraction and explanation model. Finally, our evaluations show that D-REX is simple yet effective and improves relation extraction performance of strong baseline models by 1.2-4.7%.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2109.05126/code)