METACoref: A Coreference Resolution Approach Based on Meta-information Loss for Document

Coreference resolution is a key technique in natural language processing, aiming at recognizing different representations pointing to the same entity in a text. However, in order to improve the performance, existing methods rely on single semantic features for complex representations and iterative operations on one hand, and introduce multiple complex structures and external knowledge on the other hand, which sacrifices the efficiency and generalization performance to a certain extent. Therefore, this study explores the textual meta-information and proposes a meta-information loss-based coreference resolution model, METACoref, which optimizes the task at two levels, i.e., mention recognition and coreference prediction. METACoref first enriches word representation by syntactic information and entity types, and then obtains subword-based word representation based on local and masked attention mechanisms. In mention recognition, METACoref integrates entity type features, speaker features, and belonging sentence position features to compensate for the lack of pure semantic modeling. In coreference prediction, METACoref uses a combination of dynamically balanced semantic loss and structured meta-information loss to complement the semantic information. Structured meta-information loss computes a representation of the consistency of speaker information between mentions and the relative distance between mentions. Experiments on the OntoNotes 5.0 dataset show that the method performs superiorly in mention identification and coreference prediction, significantly improving the performance of the coreference reasolution model in terms of efficiency, robustness, and long-distance dependency handling.