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.- Anthology ID:
- 2022.coling-1.408
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4610–4621
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2022.coling-1.408/
- DOI:
- Cite (ACL):
- Han Wang, Ruiliu Fu, Xuejun Zhang, Jun Zhou, and Qingwei Zhao. 2022. Ask Question First for Enhancing Lifelong Language Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4610–4621, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Ask Question First for Enhancing Lifelong Language Learning (Wang et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2022.coling-1.408.pdf
- Code
- codehan/aqf-rq
- Data
- decaNLP