Ask Question First for Enhancing Lifelong Language Learning (2022)

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

## introduction lifelong learning is the capacity of human beings to acquire, reconstruct, strengthen, and transfer knowledge @xcite . human beings can learn new knowledge while consolidating old knowledge by first detecting and learning the distinctions between old and new knowledge and then simplifying the old and new knowledge based on leveraging the common points. this concept is critical for encouraging machines to learn nlp tasks in a similar way that people do. in the application of nlp, new data are continuously acquired and categorized as either new data for existing tasks or new data for new tasks. for new data of existing tasks, the traditional method, known as isolated learning @xcite ask questions first before reading ## related work lifelong language learning (lll) is an essential step in promoting the realization of general artificial intelligence in the field of nlp. @xcite @xcite have studied lll on a single type of nlp task by regularization or replaying real data. recently, lamol @xcite uses a language model (lm) to learn various kinds of nlp tasks in qa-style. in lamol, the pseudo-data generated by the model is trained together with the new task to alleviate catastrophic forgetting. many works explored the enhancement of lamol with additional computation resources or adding sub-networks. l2kd @xcite , dnr @xcite and dfsd @xcite distilled parts or all layers of the model to improve lamol. arper @xcite applied regualarization on parameters with prioritized exemplar replay. rational-lamol @xcite applied critical freezing guided by rationale information which is obtained by human or unsupervised rationale generation @xcite . @xcite applied adapter @xcite to plug into pretrain language model. those are effective but require more computational resources (e.g., distillation and regularization) or new parameters (such as adding sub-networks for new tasks). we aspire to improve without increasing our resources. consequently, based on the metacognition @xcite @xcite proposed in education and psychology, we propose a more training-efficient and robust data format: first ask questions, then observe the context, finally answer the questions. in addition, we introduce replay questions to strengthen the model's attention to questions. ## methodology in this section, we first introduce lamol in section 3.1. then, our proposed ask question first (aqf) is detailed in section 3.2. thirdly, we introduce the replay questions in section 3.3. finally, we summarize the training objectives in our paper in section 3.4. ## conclusion and future work this work proposed aqf-rq which is a simple yet efficient and robust lifelong language learning method. we propose a new question-first data format that is train-efficient without additional computational resources and new parameters. in aqf's "bqca" format, generating q first makes it easier to generate task-corresponding pseudo-data, and a is more accurate because a is next to c. in addition, rq can strengthen the model's attention to the problem, so that the model has a greater probability of generating task-corresponding questions, and further generates more task-corresponding pseudodata. aqf-rq effectively alleviates catastrophic forgetting, only 0.36% lower than multi-task learning. due to a lack of computing resources, we did not experiment on larger datasets and longer task orders, which we leave as future work. in addition, we will investigate ways to enhance performance when the task boundary is unclear.