@inproceedings{chen-etal-2023-iterative,
title = "Iterative Document-level Information Extraction via Imitation Learning",
author = "Chen, Yunmo and
Gantt, William and
Gu, Weiwei and
Chen, Tongfei and
White, Aaron and
Van Durme, Benjamin",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.136",
doi = "10.18653/v1/2023.eacl-main.136",
pages = "1858--1874",
abstract = "We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template{'}s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks {--} 4-ary relation extraction on SciREX and template extraction on MUC-4 {--} as well as a strong baseline on the new BETTER Granular task.",
}
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<abstract>We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template’s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks – 4-ary relation extraction on SciREX and template extraction on MUC-4 – as well as a strong baseline on the new BETTER Granular task.</abstract>
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%0 Conference Proceedings
%T Iterative Document-level Information Extraction via Imitation Learning
%A Chen, Yunmo
%A Gantt, William
%A Gu, Weiwei
%A Chen, Tongfei
%A White, Aaron
%A Van Durme, Benjamin
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F chen-etal-2023-iterative
%X We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template’s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks – 4-ary relation extraction on SciREX and template extraction on MUC-4 – as well as a strong baseline on the new BETTER Granular task.
%R 10.18653/v1/2023.eacl-main.136
%U https://aclanthology.org/2023.eacl-main.136
%U https://doi.org/10.18653/v1/2023.eacl-main.136
%P 1858-1874
Markdown (Informal)
[Iterative Document-level Information Extraction via Imitation Learning](https://aclanthology.org/2023.eacl-main.136) (Chen et al., EACL 2023)
ACL
- Yunmo Chen, William Gantt, Weiwei Gu, Tongfei Chen, Aaron White, and Benjamin Van Durme. 2023. Iterative Document-level Information Extraction via Imitation Learning. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1858–1874, Dubrovnik, Croatia. Association for Computational Linguistics.