ArgGen: Prompting Text Generation Models for Document-Level Event-Argument Aggregation

Debanjana Kar, Sudeshna Sarkar, Pawan Goyal


Abstract
Most of the existing discourse-level Information Extraction tasks have been modeled to be extractive in nature. However, we argue that extracting information from larger bodies of discourse-like documents requires more natural language understanding and reasoning capabilities. In our work, we propose the novel task of document-level event argument aggregation which generates consolidated event-arguments at a document-level with minimal loss of information. More specifically, we focus on generating precise document-level information frames in a multilingual setting using prompt-based methods. In this paper, we show the effectiveness of u prompt-based text generation approach to generate document-level argument spans in a low-resource and zero-shot setting. We also release the first of its kind multilingual event argument aggregation dataset that can be leveraged in other related multilingual text generation tasks as well: https://github.com/DebanjanaKar/ArgGen.
Anthology ID:
2022.findings-aacl.37
Volume:
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
399–404
Language:
URL:
https://aclanthology.org/2022.findings-aacl.37
DOI:
Bibkey:
Cite (ACL):
Debanjana Kar, Sudeshna Sarkar, and Pawan Goyal. 2022. ArgGen: Prompting Text Generation Models for Document-Level Event-Argument Aggregation. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 399–404, Online only. Association for Computational Linguistics.
Cite (Informal):
ArgGen: Prompting Text Generation Models for Document-Level Event-Argument Aggregation (Kar et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-aacl.37.pdf