Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study

Large pre-trained language models help to achieve state of the art on a variety of natural language processing (NLP) tasks, nevertheless, they still suffer from forgetting when incrementally learning a sequence of tasks. To alleviate this problem, recent works enhance existing models by sparse experience replay and local adaption, which yield satisfactory performance. However, in this paper we find that pre-trained language models like BERT have a potential ability to learn sequentially, even without any sparse memory replay. To verify the ability of BERT to maintain old knowledge, we adopt and re-finetune single-layer probe networks with the parameters of BERT fixed. We investigate the models on two types of NLP tasks, text classification and extractive question answering. Our experiments reveal that BERT can actually generate high quality representations for previously learned tasks in a long term, under extremely sparse replay or even no replay. Continual Learning aims to obtain knowledge from a stream of data across time (Ring, 1994; Thrun, 1998; Chen & Liu, 2018). As a booming area in continual learning, task-incremental learning requires a model to learn a sequence of tasks, without forgetting previously learned knowledge. It is a practical scene to train models on a stream of tasks sequentially, avoiding to re-train on all existing data exhaustively once a new task arrives. In natural language processing, although many large-scale pre-trained language models (PLMs) have ceaselessly achieved on new records on various benchmarks, they cannot be directly deployed in a task-incremental setting. These models tend to perform poorly on previously seen tasks when learning new ones.