Recent neural network methods for Chinese zero pronoun resolution didn't take bidirectional attention between zero pronouns and candidate antecedents into consideration, and simply treated the task as a classification task, ignoring the relationship between different candidates of a zero pronoun. To solve these problems, we propose a Hierarchical Attention Network with Pairwise Loss (HAN-PL), for Chinese zero pronoun resolution. In the proposed HAN-PL, we design a two-layer attention model to generate more powerful representations for zero pronouns and candidate antecedents. Furthermore, we propose a novel pairwise loss by introducing the correct-antecedent similarity constraint and the pairwise-margin loss, making the learned model more discriminative. Extensive experiments have been conducted on OntoNotes 5.0 dataset, and our model achieves state-of-the-art performance in the task of Chinese zero pronoun resolution.
Published Date: 2020-06-02
Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)
Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved