Few-shot Intent Classification and Slot Filling with Retrieved Examples

Dian Yu, Luheng He, Yuan Zhang, Xinya Du, Panupong Pasupat, Qi Li


Abstract
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrieval-based methods for intent classification and slot filling tasks in few-shot settings. Retrieval-based methods make predictions based on labeled examples in the retrieval index that are similar to the input, and thus can adapt to new domains simply by changing the index without having to retrain the model. However, it is non-trivial to apply such methods on tasks with a complex label space like slot filling. To this end, we propose a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective. At inference time, we use the labels of the retrieved spans to construct the final structure with the highest aggregated score. Our method outperforms previous systems in various few-shot settings on the CLINC and SNIPS benchmarks.
Anthology ID:
2021.naacl-main.59
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
734–749
Language:
URL:
https://aclanthology.org/2021.naacl-main.59
DOI:
10.18653/v1/2021.naacl-main.59
Bibkey:
Cite (ACL):
Dian Yu, Luheng He, Yuan Zhang, Xinya Du, Panupong Pasupat, and Qi Li. 2021. Few-shot Intent Classification and Slot Filling with Retrieved Examples. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 734–749, Online. Association for Computational Linguistics.
Cite (Informal):
Few-shot Intent Classification and Slot Filling with Retrieved Examples (Yu et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.59.pdf
Video:
 https://aclanthology.org/2021.naacl-main.59.mp4