Papers › DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
Qi Jia, Hongru Huang, Kenny Q. Zhu
Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from language content is also a crucial step toward further understanding dialogues. Previous work mainly focuses on relation extraction between named entities in texts. In this paper, we propose the task of relation classification of interlocutors based on their dialogues. We crawled movie scripts from IMSDb, and annotated the relation labels for each session according to 13 pre-defined relationships. The annotated dataset DDRel consists of 6300 dyadic dialogue sessions between 694 pair of speakers with 53,126 utterances in total. We also construct session-level and pair-level relation classification tasks with widely-accepted baselines. The experimental results show that this task is challenging for existing models and the dataset will be useful for future research.
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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 | DDRel | BERT | Pair-level 13-class Acc | 39.73 | #1 of 1 | Archive leaderboard | report |
| Dialog Relation Extraction | DDRel | BERT | Pair-level 4-class Acc | 58.13 | #1 of 1 | Archive leaderboard | report |
| Dialog Relation Extraction | DDRel | BERT | Pair-level 6-class Acc | 42.33 | #1 of 1 | Archive leaderboard | report |
| Dialog Relation Extraction | DDRel | BERT | Session-level 13-class Acc | 39.4 | #1 of 1 | Archive leaderboard | report |
| Dialog Relation Extraction | DDRel | BERT | Session-level 4-class Acc | 47.1 | #1 of 1 | Archive leaderboard | report |
| Dialog Relation Extraction | DDRel | BERT | Session-level 6-class Acc | 41.87 | #1 of 1 | Archive leaderboard | report |
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