%0 Conference Proceedings %T D-REX: Dialogue Relation Extraction with Explanations %A Albalak, Alon %A Embar, Varun %A Tuan, Yi-Lin %A Getoor, Lise %A Wang, William Yang %Y Liu, Bing %Y Papangelis, Alexandros %Y Ultes, Stefan %Y Rastogi, Abhinav %Y Chen, Yun-Nung %Y Spithourakis, Georgios %Y Nouri, Elnaz %Y Shi, Weiyan %S Proceedings of the 4th Workshop on NLP for Conversational AI %D 2022 %8 May %I Association for Computational Linguistics %C Dublin, Ireland %F albalak-etal-2022-rex %X 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%. %R 10.18653/v1/2022.nlp4convai-1.4 %U https://aclanthology.org/2022.nlp4convai-1.4 %U https://doi.org/10.18653/v1/2022.nlp4convai-1.4 %P 34-46