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

arXiv: https://arxiv.org/abs/2012.02553 · HTML: https://mlsanity.com/paper/2012.02553/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 13 of 13)

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- [On the Influence of Gender and Race in Romantic Relationship Prediction from Large Language Models](https://mlsanity.com/paper/2410.03996/) (2024)
- [FriendBench: Benchmarking Dyadic Familiarity Inference in Humans and Multimodal Large Language Models](https://mlsanity.com/paper/2607.29602/) (2026)
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## Abstract (arXiv)

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.