Knowledge Guided Metric Learning for Few-Shot Text Classification

Dianbo Sui, Yubo Chen, Binjie Mao, Delai Qiu, Kang Liu, Jun Zhao


Abstract
Humans can distinguish new categories very efficiently with few examples, largely due to the fact that human beings can leverage knowledge obtained from relevant tasks. However, deep learning based text classification model tends to struggle to achieve satisfactory performance when labeled data are scarce. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate different metrics for different tasks. Armed with this network, similar tasks can use similar metrics while different tasks use different metrics. Through experiments, we demonstrate that our method outperforms the SoTA few-shot text classification models.
Anthology ID:
2021.naacl-main.261
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
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3266–3271
Language:
URL:
https://aclanthology.org/2021.naacl-main.261
DOI:
10.18653/v1/2021.naacl-main.261
Bibkey:
Cite (ACL):
Dianbo Sui, Yubo Chen, Binjie Mao, Delai Qiu, Kang Liu, and Jun Zhao. 2021. Knowledge Guided Metric Learning for Few-Shot Text Classification. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3266–3271, Online. Association for Computational Linguistics.
Cite (Informal):
Knowledge Guided Metric Learning for Few-Shot Text Classification (Sui et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.261.pdf
Video:
 https://aclanthology.org/2021.naacl-main.261.mp4
Data
NELL