Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition
Junhao Zheng, Zhanxian Liang, Haibin Chen, Qianli Ma · 2022
Continual Learning for Named Entity Recognition (CL-NER) aims to learn a growing number of entity types over time from a stream of data.However, simply learning Other-Class in the same way as new entity types amplifies the catastrophic forgetting and leads to a substantial performance drop.The main cause behind this is that Other-Class samples usually contain old entity types, and the old knowledge in these Other-Class samples is not preserved properly.Thanks to the causal inference, we identify that the forgetting is caused by the missing causal effect from the old data.To this end, we propose a unified causal framework to retrieve the causality from both new entity types and Other-Class.Furthermore, we apply curriculum learning to mitigate the impact of label noise and introduce a self-adaptive weight for balancing the causal effects between new entity types and Other-Class.Experimental results on three benchmark datasets show that our method outperforms the state-of-theart method by a large margin.Moreover, our method can be combined with the existing stateof-the-art methods to improve the performance in CL-NER. 1