DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues

Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from content also a crucial step toward further understanding dialogues. Previous work mainly focuses on relation extraction between named entities texts or within single dialogue session. In this paper, we propose the task classification interlocutors based their We crawled movie scripts IMSDb, annotated label for each session according to 13 pre-defined relationships. The dataset DDRel consists 6,300 dyadic sessions 694 pairs speakers with 53,126 utterances total. construct session-level pair-level tasks widely-accepted baselines. experimental results show that both are challenging existing models will be useful future research.