D-REX: Dialogue Relation Extraction with Explanations

TL;DR

D-REX is a semi-supervised, model-agnostic framework that improves dialogue relation extraction by providing explanations and ranking relations, achieving state-of-the-art performance and human-preferred explanations.

Contribution

The paper introduces D-REX, a novel policy-guided semi-supervised approach that incorporates explanations into relation extraction in conversations, a gap not addressed by prior work.

Findings

D-REX outperforms existing methods with a 13.5% F1 score improvement.

Human annotators prefer D-REX's explanations over other models 90% of the time.

D-REX effectively combines relation extraction and explanation generation in a semi-supervised setting.

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 data. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that explains and ranks relations. 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 about 90% of the time, human annotators prefer D-REX's explanations over a strong BERT-based joint relation extraction and explanation model. Finally, our evaluations on a dialogue relation extraction dataset show that our method is simple yet effective and achieves a state-of-the-art F1 score on relation extraction, improving upon existing methods by 13.5%.