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arXiv · 2210.06600

Iterative Document-level Information Extraction via Imitation Learning

Yunmo Chen·William Gantt·Weiwei Gu·Tongfei Chen·Aaron Steven White·Benjamin Van Durme
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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.

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Yunmo Chen, William Gantt, Weiwei Gu, Tongfei Chen, Aaron Steven White, Benjamin Van Durme. 2022-10-12. Iterative Document-level Information Extraction via Imitation Learning. https://arxiv.org/abs/2210.06600

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