论文标题
DDREL：在二元对话中进行人际关系分类的新数据集
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
论文作者
论文摘要
人际语言风格在对话中转移是人类有趣而几乎是本能的能力。了解语言内容的人际关系也是进一步理解对话的关键步骤。先前的工作主要集中于文本中指定实体之间的关系提取。在本文中，我们根据他们的对话提出了对话者的关系分类的任务。我们从IMSDB爬了电影脚本，并根据13个预定义的关系注释了每个会话的关系标签。注释的数据集DDREL由6300个二元对话会话组成，共有694对扬声器，总共有53,126个话语。我们还使用广泛认可的基线构建会话级和配对关系分类任务。实验结果表明，这项任务对于现有模型具有挑战性，数据集将对未来的研究有用。
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.
