Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction

Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Event Extraction ACE2005 Text2Event - T5-large Trigger Cl 71.9 # 1
Argument Cl 53.8 # 3
Event Extraction ACE2005 Text2Event - T5-base Trigger Cl 69.2 # 3
Argument Cl 49.8 # 4


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