PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models (2020.tacl-1)
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| Challenge: | PERL is a representation learning model that uses labeled data from the source domain and unlabeled data not necessarily drawn from the target domain. |
| Approach: | They propose a model that extends contextualized word embedding models with pivot-based fine-tuning to address this bottleneck. |
| Outcome: | The proposed model outperforms strong baselines across 22 sentiment classification domain adaptation setups and improves in-domain model performance. |
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