Joint Entity and Event Extraction with Generative Adversarial Imitation Learning

We propose a new framework for entity and event extraction based on generative adversarial imitation learning—an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties...

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Published inData intelligence Vol. 1; no. 2; pp. 99 - 120
Main Authors Zhang, Tongtao, Ji, Heng, Sil, Avirup
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.04.2019
MIT Press Journals, The
Subjects
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Event extraction
Generative adversarial network
Generative adversarial networks
Imitation learning
Information extraction
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Summary:We propose a new framework for entity and event extraction based on generative adversarial imitation learning—an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse. We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth (expert) and the extractor (agent). Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods.
Bibliography:Spring, 2019
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ISSN:2641-435X
2641-435X
DOI:10.1162/dint_a_00014