LED: A Dataset for Life Event Extraction from Dialogs
This work addresses the problem of extracting personal life events from daily conversations for applications like personalized recommendations, but it is incremental as it builds on existing information extraction frameworks.
The paper introduces LED, a dataset for extracting life events from conversational data, and establishes a new task for conversational life event extraction, finding that current event extraction models perform poorly on this task.
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.