Abstract
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text. While successful, this approach is expensive in terms of hardware, runtime and CO 2 emissions. Here, we propose a cheaper alternative: We train Word2Vec on target-domain text and align the resulting word vectors with the wordpiece vectors of a general-domain PTLM. We evaluate on eight English biomedical Named Entity Recognition (NER) tasks and compare against the recently proposed BioBERT model. We cover over 60% of the BioBERT - BERT F1 delta, at 5% of BioBERT’s CO 2 footprint and 2% of its cloud compute cost. We also show how to quickly adapt an existing general-domain Question Answering (QA) model to an emerging domain: the Covid-19 pandemic.- Anthology ID:
- 2020.findings-emnlp.134
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1482–1490
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.134
- DOI:
- 10.18653/v1/2020.findings-emnlp.134
- Cite (ACL):
- Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2020. Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1482–1490, Online. Association for Computational Linguistics.
- Cite (Informal):
- Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA (Poerner et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2020.findings-emnlp.134.pdf
- Code
- npoe/covid-qa
- Data
- BC5CDR, SQuAD