Lifelogging has gained more attention due to its wide applications, such as personalized recommendations or memory assistance. The issues of collecting and extracting personal life events have emerged. People often share their life experiences with others through conversations. However, extracting life events from conversations is rarely explored. In this paper, we present Life Event Dialog, a dataset containing fine-grained life event annotations on conversational data. In addition, we initiate a novel conversational life event extraction task and differentiate the task from the public event extraction or the life event extraction from other sources like microblogs. We explore three information extraction (IE) frameworks to address the conversational life event extraction task: OpenIE, relation extraction, and event extraction. A comprehensive empirical analysis of the three baselines is established. The results suggest that the current event extraction model still struggles with extracting life events from human daily conversations. Our proposed life event dialog dataset and in-depth analysis of IE frameworks will facilitate future research on life event extraction from conversations.

翻译：生活日志记录因其在个性化推荐、记忆辅助等领域的广泛应用而日益受到关注。个人生活事件的收集与提取问题随之浮现。人们常通过对话分享生活经历，然而从对话中提取生活事件的研究尚属空白。本文提出生活事件对话数据集（Life Event Dialog），该数据集包含对话数据中细粒度生活事件标注。我们进一步定义了新颖的对话式生活事件抽取任务，并将其与公开事件抽取或微博等其他来源的生活事件抽取进行区分。为处理对话式生活事件抽取任务，我们探索了三种信息抽取框架：开放信息抽取、关系抽取和事件抽取。基于这三个基线建立了全面的实证分析，结果表明当前事件抽取模型在从人类日常对话中提取生活事件时仍存在困难。本文提出的生活事件对话数据集及对信息抽取框架的深入分析，将推动从对话中抽取生活事件的后续研究。