  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%.
