Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder

• URL: http://arxiv.org/abs/2107.00189v1
• Date: Thu, 1 Jul 2021 02:55:12 GMT
• Title: Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder
• Authors: Xiangyu Xi, Wei Ye, Shikun Zhang, Quanxiu Wang, Huixing Jiang, Wei Wu
• Abstract summary: We formalize event argument extraction (EAE) as a Seq2Seq-like learning problem for the first time. A neural architecture with a novel Bi-directional Entity-level Recurrent Decoder (BERD) is proposed to generate argument roles.
• Score: 7.60457018063735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The argument role type information of contextual entities is mainly utilized as training signals, ignoring the potential merits of directly adopting it as semantically rich input features; 2) The argument-level sequential semantics, which implies the overall distribution pattern of argument roles over an event mention, is not well characterized. To tackle the above two bottlenecks, we formalize EAE as a Seq2Seq-like learning problem for the first time, where a sentence with a specific event trigger is mapped to a sequence of event argument roles. A neural architecture with a novel Bi-directional Entity-level Recurrent Decoder (BERD) is proposed to generate argument roles by incorporating contextual entities' argument role predictions, like a word-by-word text generation process, thereby distinguishing implicit argument distribution patterns within an event more accurately.

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