Abstract
Recent research considers few-shot intent detection as a meta-learning problem: the model is learning to learn from a consecutive set of small tasks named episodes. In this work, we propose ProtAugment, a meta-learning algorithm for short texts classification (the intent detection task). ProtAugment is a novel extension of Prototypical Networks, that limits overfitting on the bias introduced by the few-shots classification objective at each episode. It relies on diverse paraphrasing: a conditional language model is first fine-tuned for paraphrasing, and diversity is later introduced at the decoding stage at each meta-learning episode. The diverse paraphrasing is unsupervised as it is applied to unlabelled data, and then fueled to the Prototypical Network training objective as a consistency loss. ProtAugment is the state-of-the-art method for intent detection meta-learning, at no extra labeling efforts and without the need to fine-tune a conditional language model on a given application domain.- Anthology ID:
- 2021.acl-long.191
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2454–2466
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.191
- DOI:
- 10.18653/v1/2021.acl-long.191
- Cite (ACL):
- Thomas Dopierre, Christophe Gravier, and Wilfried Logerais. 2021. PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2454–2466, Online. Association for Computational Linguistics.
- Cite (Informal):
- PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning (Dopierre et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.acl-long.191.pdf
- Code
- tdopierre/ProtAugment
- Data
- BANKING77, DialoGLUE, HWU64