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


  • Qi Jia Shanghai Jiao Tong University
  • Hongru Huang Shanghai Jiao Tong University
  • Kenny Q. Zhu Shanghai Jiao Tong University


Text Classification & Sentiment Analysis, Information Extraction


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 or within a single dialogue session. 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 label for each session according to 13 pre-defined relationships. The annotated dataset DDRel consists of 6,300 dyadic dialogue sessions between 694 pairs 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 both tasks are challenging for existing models and the dataset will be useful for future research.




How to Cite

Jia, Q., Huang, H., & Q. Zhu, K. (2021). DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 13125-13133. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17551



AAAI Technical Track on Speech and Natural Language Processing I