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    <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.</abstract>
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%0 Conference Proceedings
%T Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder
%A Xiangyu, Xi
%A Ye, Wei
%A Zhang, Shikun
%A Wang, Quanxiu
%A Jiang, Huixing
%A Wu, Wei
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F xiangyu-etal-2021-capturing
%X 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.
%R 10.18653/v1/2021.acl-long.18
%U https://aclanthology.org/2021.acl-long.18/
%U https://doi.org/10.18653/v1/2021.acl-long.18
%P 210-219

Markdown (Informal)

[Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder](https://aclanthology.org/2021.acl-long.18/) (Xiangyu et al., ACL-IJCNLP 2021)

• Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder (Xiangyu et al., ACL-IJCNLP 2021)

ACL

• Xi Xiangyu, Wei Ye, Shikun Zhang, Quanxiu Wang, Huixing Jiang, and Wei Wu. 2021. Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 210–219, Online. Association for Computational Linguistics.