Ask Question First for Enhancing Lifelong Language Learning

• URL: http://arxiv.org/abs/2208.08367v1
• Date: Wed, 17 Aug 2022 15:58:33 GMT
• Title: Ask Question First for Enhancing Lifelong Language Learning
• Authors: Han Wang, Ruiliu Fu, Xuejun Zhang, Jun Zhou, Qingwei Zhao
• Abstract summary: Lifelong language learning aims to stream learning NLP tasks while retaining knowledge of previous tasks. Previous works have explored formatting all data as "begin token (textitB) + context (textitC) + question (textitQ + answer (textitA)" for different tasks. We propose the Ask Question First and Replay Question (AQF-RQ), including a novel data format "textitBQCA" and a new training task to train pseudo questions of previous tasks.
• Score: 12.766084031891815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lifelong language learning aims to stream learning NLP tasks while retaining knowledge of previous tasks. Previous works based on the language model and following data-free constraint approaches have explored formatting all data as "begin token (\textit{B}) + context (\textit{C}) + question (\textit{Q}) + answer (\textit{A})" for different tasks. However, they still suffer from catastrophic forgetting and are exacerbated when the previous task's pseudo data is insufficient for the following reasons: (1) The model has difficulty generating task-corresponding pseudo data, and (2) \textit{A} is prone to error when \textit{A} and \textit{C} are separated by \textit{Q} because the information of the \textit{C} is diminished before generating \textit{A}. Therefore, we propose the Ask Question First and Replay Question (AQF-RQ), including a novel data format "\textit{BQCA}" and a new training task to train pseudo questions of previous tasks. Experimental results demonstrate that AQF-RQ makes it easier for the model to generate more pseudo data that match corresponding tasks, and is more robust to both sufficient and insufficient pseudo-data when the task boundary is both clear and unclear. AQF-RQ can achieve only 0.36\% lower performance than multi-task learning.

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