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Event detection (ED) identifies and classifies event triggers fromunstructured texts, serving as a fundamental task for information extraction.Despite the remarkable progress achieved in the past several years, mostresearch efforts focus on detecting events from formal texts (e.g., newsarticles, Wikipedia documents, financial announcements). Moreover, the texts ineach dataset are either from a single source or multiple yet relativelyhomogeneous sources. With massive amounts of user-generated text accumulatingon the Web and inside enterprises, identifying meaningful events in theseinformal texts, usually from multiple heterogeneous sources, has become aproblem of significant practical value. As a pioneering exploration thatexpands event detection to the scenarios involving informal and heterogeneoustexts, we propose a new large-scale Chinese event detection dataset based onuser reviews, text conversations, and phone conversations in a leadinge-commerce platform for food service. We carefully investigate the proposeddataset's textual informality and multi-source heterogeneity characteristics byinspecting data samples quantitatively and qualitatively. Extensive experimentswith state-of-the-art event detection methods verify the unique challengesposed by these characteristics, indicating that multi-source informal eventdetection remains an open problem and requires further efforts. Our benchmarkand code are released at \url{https://github.com/myeclipse/MUSIED}.

MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts