Event detection (ED), which identifies event trigger words and classifies event typesaccording to contexts , is the first and most fundamental step for extracting event knowledge from plain text. Most existing datasets exhibit the following issues that limit further development of ED: (1) Small scale of existing datasets is not sufficient for training and stably benchmarking increasingly sophisticated modern neural methods. (2)  Limited event types of existing datasets lead to the trained models cannot be easily adapted to general-domain scenarios . To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 117,200 event mention instances, and 207 event types. MAVEN alleviates the lack of data problem and covers much more general event types. Besides the dataset, we reproduce the recent state-of-the-art ED models and conduct a thorough evaluation for these models on MAVEN. The experimental results and empirical analyses show that existing ED methods cannot achieve promising results  as on the small datasets, which suggests ED in real world remains a challenging task and requires further research efforts. The dataset and baseline code will be released in the future to promote this field.
Event detection (ED), which means identifying event trigger words and classifying event types , is the first and most fundamental step for extracting event knowledge from plain text. Most existing datasets exhibit the following issues that limit further development of ED: (1) Data scarcity. Existing small-scale datasets are not sufficient for training and stably benchmarking increasingly sophisticated modern neural methods. (2) Low coverage. Limited event types of existing datasets cannot well cover general-domain events, which restricts the applications of ED models . To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 118,732 event mention instances, and 168 event types. MAVEN alleviates the data scarcity problem and covers much more general event types. We reproduce the recent state-of-the-art ED models and conduct a thorough evaluation on MAVEN. The experimental results show that existing ED methods cannot achieve promising results on MAVEN as on the small datasets, which suggests that ED in the real world remains a challenging task and requires further research efforts. We also discuss further directions for general domain ED with empirical analyses. The source code and dataset can be obtained from URL
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classifies event typesaccording to contexts
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lead to the trained models cannot be easily adapted to
cannot well cover
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scenarios
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lack of data
data scarcity
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and empirical analyses
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on MAVEN
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The dataset and baseline code will be released in the future to promote this field.
We also discuss further directions for general domain ED with empirical analyses. The source code and dataset can be obtained from URL