short-paper

Multi-Sentence Argument Linking via An Event-Aware Hierarchical Encoder

Authors:

Taifeng WangAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 3578 - 3582

https://doi.org/10.1145/3459637.3482148

Published: 30 October 2021 Publication History

Abstract

Multi-sentence argument linking aims at detecting implicit event arguments across sentences, which is indispensable when textual events span across multiple sentences in a document. Previous studies suffer from the inherent limitations of error propagation and lack the explicit modeling of the local and non-local interactions in a textual event. In this paper, we propose an event-aware hierarchical encoder for multi-sentence argument linking. Specifically, we introduce a hierarchical encoder to explicitly capture the local and global interactions in a textual event. Furthermore, we introduce an auxiliary task to predict the event-relevant context in a manner of multi-task learning, which can implicitly benefit the argument linking model to be aware of the event-relevant context. The empirical results on the widely used argument linking dataset show that our model significantly outperforms the baselines, which demonstrates the effectiveness of our proposed method.

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Cited By

Ahmed ULin JGarcia Diaz V(2024)Automatically Temporal Labeled Data Generation Using Positional Lexicon Expansion for Focus Time Estimation of News ArticlesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/356816423:5(1-20)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3568164
Ahmed ULin JSrivastava G(2023)Semisupervised Federated Learning for Temporal News Hyperpatism DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324760210:4(1758-1769)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2023.3247602

Index Terms

Multi-Sentence Argument Linking via An Event-Aware Hierarchical Encoder
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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Beijing Academy of Artificial Intelligence
National Natural Science Foundation of China

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Cited By

Ahmed ULin JGarcia Diaz V(2024)Automatically Temporal Labeled Data Generation Using Positional Lexicon Expansion for Focus Time Estimation of News ArticlesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/356816423:5(1-20)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3568164
Ahmed ULin JSrivastava G(2023)Semisupervised Federated Learning for Temporal News Hyperpatism DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324760210:4(1758-1769)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2023.3247602

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