Class incremental named entity recognition without forgetting

Abstract

Class Incremental Named Entity Recognition (CINER) needs to learn new entity classes without forgetting old entity classes under the setting where the data only contain annotations for new entity classes. As is well known, the forgetting problem is the biggest challenge in Class Incremental Learning (CIL). In the CINER scenario, the unlabeled old class entities will further aggravate the forgetting problem. The current CINER method based on a single model cannot completely avoid the forgetting problem and is sensitive to the learning order of entity classes. To this end, we propose a Multi-Model (MM) framework that trains a new model for each incremental step and uses all the models for inference. In MM, each model only needs to learn the entity classes included in corresponding step, so MM has no forgetting problem and is robust to the different entity class learning orders. Furthermore, we design an error-correction training strategy and conflict-handling rules for MM to further improve performance. We evaluate MM on CoNLL-03 and OntoNotes-V5, and the experimental results show that our framework outperforms the current state-of-the-art (SOTA) methods by a large margin.