Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder
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.
Cite
@article{arxiv.2107.00189,
title = {Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder},
author = {Xiangyu Xi and Wei Ye and Shikun Zhang and Quanxiu Wang and Huixing Jiang and Wei Wu},
journal= {arXiv preprint arXiv:2107.00189},
year = {2021}
}