Abstract
On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Nevertheless, their huge model size and low inference speed have hindered the deployment on resource-limited devices in practice. In this paper, we target to compress PLMs with knowledge distillation, and propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information. Specifically, to enhance the model capability and transferability, we leverage the idea of meta-learning and set up domain-relational graphs to capture the relational information across different domains. And to dynamically select the most representative prototypes for each domain, we propose a hierarchical compare-aggregate mechanism to capture hierarchical relationships. Extensive experiments on public multi-domain datasets demonstrate the superior performance of our HRKD method as well as its strong few-shot learning ability. For reproducibility, we release the code at https://github.com/cheneydon/hrkd.- Anthology ID:
- 2021.emnlp-main.250
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3126–3136
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.250
- DOI:
- 10.18653/v1/2021.emnlp-main.250
- Cite (ACL):
- Chenhe Dong, Yaliang Li, Ying Shen, and Minghui Qiu. 2021. HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3126–3136, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression (Dong et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.emnlp-main.250.pdf
- Code
- cheneydon/hrkd
- Data
- MultiNLI