Abstract
Pivot-based neural representation models have led to significant progress in domain adaptation for NLP. However, previous research following this approach utilize only labeled data from the source domain and unlabeled data from the source and target domains, but neglect to incorporate massive unlabeled corpora that are not necessarily drawn from these domains. To alleviate this, we propose PERL: A representation learning model that extends contextualized word embedding models such as BERT (Devlin et al., 2019) with pivot-based fine-tuning. PERL outperforms strong baselines across 22 sentiment classification domain adaptation setups, improves in-domain model performance, yields effective reduced-size models, and increases model stability.1- Anthology ID:
- 2020.tacl-1.33
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 8
- Month:
- Year:
- 2020
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 504–521
- Language:
- URL:
- https://aclanthology.org/2020.tacl-1.33
- DOI:
- 10.1162/tacl_a_00328
- Cite (ACL):
- Eyal Ben-David, Carmel Rabinovitz, and Roi Reichart. 2020. PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models. Transactions of the Association for Computational Linguistics, 8:504–521.
- Cite (Informal):
- PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models (Ben-David et al., TACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.tacl-1.33.pdf