Meta Distant Transfer Learning for Pre-trained Language Models
Chengyu Wang, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, Yin Zhang
Abstract
With the wide availability of Pre-trained Language Models (PLMs), multi-task fine-tuning across domains has been extensively applied. For tasks related to distant domains with different class label sets, PLMs may memorize non-transferable knowledge for the target domain and suffer from negative transfer. Inspired by meta-learning, we propose the Meta Distant Transfer Learning (Meta-DTL) framework to learn the cross-task knowledge for PLM-based methods. Meta-DTL first employs task representation learning to mine implicit relations among multiple tasks and classes. Based on the results, it trains a PLM-based meta-learner to capture the transferable knowledge across tasks. The weighted maximum entropy regularizers are proposed to make meta-learner more task-agnostic and unbiased. Finally, the meta-learner can be fine-tuned to fit each task with better parameter initialization. We evaluate Meta-DTL using both BERT and ALBERT on seven public datasets. Experiment results confirm the superiority of Meta-DTL as it consistently outperforms strong baselines. We find that Meta-DTL is highly effective when very few data is available for the target task.- Anthology ID:
- 2021.emnlp-main.768
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9742–9752
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.768
- DOI:
- 10.18653/v1/2021.emnlp-main.768
- Cite (ACL):
- Chengyu Wang, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, and Yin Zhang. 2021. Meta Distant Transfer Learning for Pre-trained Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9742–9752, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Meta Distant Transfer Learning for Pre-trained Language Models (Wang et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2021.emnlp-main.768.pdf
- Data
- IMDb Movie Reviews, MultiNLI, SST, SST-5