Abstract
This paper investigates whether the power of the models pre-trained on text data, such as BERT, can be transferred to general token sequence classification applications. To verify pre-trained models’ transferability, we test the pre-trained models on text classification tasks with meanings of tokens mismatches, and real-world non-text token sequence classification data, including amino acid, DNA, and music. We find that even on non-text data, the models pre-trained on text converge faster, perform better than the randomly initialized models, and only slightly worse than the models using task-specific knowledge. We also find that the representations of the text and non-text pre-trained models share non-trivial similarities.- Anthology ID:
- 2021.findings-emnlp.189
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2195–2208
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.189
- DOI:
- 10.18653/v1/2021.findings-emnlp.189
- Cite (ACL):
- Wei-Tsung Kao and Hung-yi Lee. 2021. Is BERT a Cross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trained Models’ Transferability. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2195–2208, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Is BERT a Cross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trained Models’ Transferability (Kao & Lee, Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.findings-emnlp.189.pdf
- Data
- GLUE