Several works employed domain adaptation to improve performance. For example, Du et al. (2020) approached the sentiment analysis task by using a BERT-based (Devlin et al., 2019) feature extractor alongside domain adaptation.
Furthermore, McHardy et al. (2019) used domain adaptation for satire detection, with the publication source representing the domain. At the same time, Dayanik and Padó (2020) used a technique similar to domain adaptation, this time for political claims detection.
The previous approaches consisted of actor masking, as well as adversarial debiasing and sample weighting. Other studies considering domain adaptation included suggestion mining (Klimaszewski and Andruszkiewicz, 2019), mixup synthesis training (Tang et al., 2020), and effective regularization (Vernikos et al., 2020).
Reference
- Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao. 2020. Adversarial and domain-aware bert for cross-domain sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4019–4028.
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186.
- Robert McHardy, Heike Adel, and Roman Klinger. 2019. Adversarial training for satire detection: Controlling for confounding variables. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 660–665.
- Erenay Dayanik and Sebastian Padó. 2020. Masking actor information leads to fairer political claims detection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4385–4391.
- Mateusz Klimaszewski and Piotr Andruszkiewicz. 2019. Wut at semeval-2019 task 9: Domainadversarial neural networks for domain adaptation in suggestion mining. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1262–1266.
- Yuhua Tang, Zhipeng Lin, Haotian Wang, and Liyang Xu. 2020. Adversarial mixup synthesis training for unsupervised domain adaptation. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3727–3731. IEEE.
- Giorgos Vernikos, Katerina Margatina, Alexandra Chronopoulou, and Ion Androutsopoulos. 2020. Domain adversarial fine-tuning as an effective regularizer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 3103–3112.