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.
Zhang et al. REF proposes a new framework for event extraction based on an inverse reinforcement learning method using a generative adversarial network (GAN), and delves into the influence of two different pre-training methods of ELMo and word2vec on the proposed model.
Joint Entity and Event Extraction with Generative Adversarial Imitation Learning
Data Intelligence
Data Intelligence
Computer Science