Abstract
Intermediate training of pre-trained transformer-based language models on domain-specific data leads to substantial gains for downstream tasks. To increase efficiency and prevent catastrophic forgetting alleviated from full domain-adaptive pre-training, approaches such as adapters have been developed. However, these require additional parameters for each layer, and are criticized for their limited expressiveness. In this work, we introduce TADA, a novel task-agnostic domain adaptation method which is modular, parameter-efficient, and thus, data-efficient. Within TADA, we retrain the embeddings to learn domain-aware input representations and tokenizers for the transformer encoder, while freezing all other parameters of the model. Then, task-specific fine-tuning is performed. We further conduct experiments with meta-embeddings and newly introduced meta-tokenizers, resulting in one model per task in multi-domain use cases. Our broad evaluation in 4 downstream tasks for 14 domains across single- and multi-domain setups and high- and low-resource scenarios reveals that TADA is an effective and efficient alternative to full domain-adaptive pre-training and adapters for domain adaptation, while not introducing additional parameters or complex training steps.- Anthology ID:
- 2023.findings-acl.31
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 487–503
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.31
- DOI:
- 10.18653/v1/2023.findings-acl.31
- Cite (ACL):
- Chia-Chien Hung, Lukas Lange, and Jannik Strötgen. 2023. TADA: Efficient Task-Agnostic Domain Adaptation for Transformers. In Findings of the Association for Computational Linguistics: ACL 2023, pages 487–503, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TADA: Efficient Task-Agnostic Domain Adaptation for Transformers (Hung et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.findings-acl.31.pdf