Ask Question First for Enhancing Lifelong Language Learning

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 ($\mathit{\text{B}}$) + context ($\mathit{\text{C}}$) + question ($\mathit{\text{Q}}$) + answer ($\mathit{\text{A}}$)” 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) $\mathit{\text{A}}$ is prone to error when $\mathit{\text{A}}$ and $\mathit{\text{C}}$ are separated by $\mathit{\text{Q}}$ because the information of the $\mathit{\text{C}}$ is diminished before generating $\mathit{\text{A}}$. Therefore, we propose the Ask Question First and Replay Question (AQF-RQ), including a novel data format “$\mathit{\text{BQCA}}$” 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.