Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Sara Meftah, Youssef Tamaazousti, Nasredine Semmar, Hassane Essafi, Fatiha Sadat
Abstract
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon target-specific patterns. In this paper, we propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining the valuable source knowledge. Our experiments on POS tagging of social media texts (Tweets domain) demonstrate that our method achieves state-of-the-art performances on 3 commonly used datasets.- Anthology ID:
- N19-1416
- Volume:
- 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)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4107–4112
- Language:
- URL:
- https://aclanthology.org/N19-1416
- DOI:
- 10.18653/v1/N19-1416
- Cite (ACL):
- Sara Meftah, Youssef Tamaazousti, Nasredine Semmar, Hassane Essafi, and Fatiha Sadat. 2019. Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging. 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 4107–4112, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging (Meftah et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/N19-1416.pdf
- Data
- Tweebank