Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding

By Sijia Wang, Mo Yu, Shiyu Chang, Lichao Sun, Lifu Huang. 2021

72 citations recorded by the source corpus.

Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols. These approaches are usually limited to a set of pre-defined types. We propose a novel event extraction framework that uses event types and argument roles as natural language queries to extract candidate triggers and arguments from the input text. With the rich semantics in the queries, our framework benefits from the attention mechanisms to better capture the semantic correlation between the event types or argument roles and the input text. Furthermore, the query-and-extract formulation allows our approach to leverage all available event annotations from various ontologies as a unified model. Experiments on ACE and ERE demonstrate that our approach achieves state-of-the-art performance on each dataset and significantly outperforms existing methods on zero-shot event extraction.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Lifu Huang
2. Shiyu Chang
3. Mo Yu
4. Sijia Wang
5. Minqian Liu
6. Zhiyuan Liu
7. Maosong Sun
8. Mohit Bansal

Citations in the corpus

• Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (2022, background): “Lifelong Event Detection Deep neural networks have shown state-of-the-art performance on supervised event detection (Nguyen et al., 2016; Feng et al., 2016; Zhang et al., 2017; Huang and Ji, 2020; Wang et al., 2021b).”
• The Art of Prompting: Event Detection based on Type Specific Prompts (2022, methodology): “…Liao and Grishman, 2010; McClosky et al., 2011; Li et al., 2013; Chen et al., 2015; Cao et al., 2015; Feng et al., 2016; Yang and Mitchell, 2016; Nguyen et al., 2016; Zhang et al., 2017; Lin et al., 2020; Wang et al., 2021b), however, they cannot be directly applied to detect new types of events.”