Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling
Samyadeep Basu, Amr Sharaf, Karine Ip Kiun Chong, Alex Fischer, Vishal Rohra, Michael Amoake, Hazem El-Hammamy, Ehi Nosakhare, Vijay Ramani, Benjamin Han
Abstract
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such systems are not scalable. This problem can be addressed by learning from few examples using fast supervised meta-learning techniques such as prototypical networks. In this work, we systematically investigate how contrastive learning and data augmentation methods can benefit these existing meta-learning pipelines for jointly modelled IC/SF tasks. Through extensive experiments across standard IC/SF benchmarks (SNIPS and ATIS), we show that our proposed approaches outperform standard meta-learning methods: contrastive losses as a regularizer in conjunction with prototypical networks consistently outperform the existing state-of-the-art for both IC and SF tasks, while data augmentation strategies primarily improve few-shot IC by a significant margin- Anthology ID:
- 2022.suki-1.3
- Volume:
- Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, USA
- Editors:
- Wenhu Chen, Xinyun Chen, Zhiyu Chen, Ziyu Yao, Michihiro Yasunaga, Tao Yu, Rui Zhang
- Venue:
- SUKI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 17–25
- Language:
- URL:
- https://aclanthology.org/2022.suki-1.3
- DOI:
- 10.18653/v1/2022.suki-1.3
- Cite (ACL):
- Samyadeep Basu, Amr Sharaf, Karine Ip Kiun Chong, Alex Fischer, Vishal Rohra, Michael Amoake, Hazem El-Hammamy, Ehi Nosakhare, Vijay Ramani, and Benjamin Han. 2022. Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling. In Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI), pages 17–25, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling (Basu et al., SUKI 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.suki-1.3.pdf
- Data
- ATIS