Papers › MLBiNet: A Cross-Sentence Collective Event Detection Network

MLBiNet: A Cross-Sentence Collective Event Detection Network

20 May 2021ACL 2021 5arXiv:2105.09458archive 2025-07-28

Dongfang Lou, Zhilin Liao, Shumin Deng, Ningyu Zhang, Huajun Chen

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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DecoderEvent DetectionEvent ExtractionSentenceTAG

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LSTMSeq2SeqSigmoid ActivationTanh Activation

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