Papers › D-REX: Dialogue Relation Extraction with Explanations
D-REX: Dialogue Relation Extraction with Explanations
Alon Albalak, Varun Embar, Yi-Lin Tuan, Lise Getoor, William Yang Wang
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%.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Dialog Relation Extraction | DialogRE | D-REX_RoBERTa | F1 (v2) | 67.2 | #7 of 17 | Archive leaderboard | report |
| Dialog Relation Extraction | DialogRE | Joint_RoBERTa | F1 (v2) | 65.2 | #11 of 17 | Archive leaderboard | report |
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