We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Considering the timeliness of communication in a dialogue, we design a new metric to evaluate the performance of RE methods in a conversational setting and investigate the performance of several representative RE methods on DialogRE. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings. DialogRE is available at https://dataset.org/dialogre/.

本文介绍了第一个人工注释的基于对话的关系抽取（RE）数据集DialogRE，用于支持在对话中预测两个参数之间的关系，尤其是涉及多个句子的跨句子RE任务，并基于分析指出演讲者相关信息在该任务中发挥了关键作用。在提出一个新的评估指标以评估在会话环境中进行RE方法的性能之后，本文研究了几种常用的RE方法在DialogRE数据集上的表现，并通过实验证明，最佳表现模型的扩展可以在标准和会话环境的评估设置中都取得收益。