MLBiNet: A Cross-Sentence Collective Event Detection Network

• URL: http://arxiv.org/abs/2105.09458v2
• Date: Sun, 23 May 2021 10:16:22 GMT
• Title: MLBiNet: A Cross-Sentence Collective Event Detection Network
• Authors: Dongfang Lou, Zhilin Liao, Shumin Deng, Ningyu Zhang, Huajun Chen
• Abstract summary: We propose a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level association of events and semantic information simultaneously. We show that our approach provides significant improvement in performance compared to the current state-of-the-art results.
• Score: 9.929840613301987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of collectively detecting multiple events, particularly in cross-sentence settings. The key to dealing with the problem is to encode semantic information and model event inter-dependency at a document-level. In this paper, we reformulate it as a Seq2Seq task and propose a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level association of events and semantic information simultaneously. Specifically, a bidirectional decoder is firstly devised to model event inter-dependency within a sentence when decoding the event tag vector sequence. Secondly, an information aggregation module is employed to aggregate sentence-level semantic and event tag information. Finally, we stack multiple bidirectional decoders and feed cross-sentence information, forming a multi-layer bidirectional tagging architecture to iteratively propagate information across sentences. We show that our approach provides significant improvement in performance compared to the current state-of-the-art results.

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