Dynamic Memory Induction Networks for Few-Shot Text Classification

Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, Xiaodan Zhu


Abstract
This paper proposes Dynamic Memory Induction Networks (DMIN) for few-short text classification. The model develops a dynamic routing mechanism over static memory, enabling it to better adapt to unseen classes, a critical capability for few-short classification. The model also expands the induction process with supervised learning weights and query information to enhance the generalization ability of meta-learning. The proposed model brings forward the state-of-the-art performance significantly by 2~4% improvement on the miniRCV1 and ODIC datasets. Detailed analysis is further performed to show how the proposed network achieves the new performance.
Anthology ID:
2020.acl-main.102
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1087–1094
Language:
URL:
https://aclanthology.org/2020.acl-main.102
DOI:
10.18653/v1/2020.acl-main.102
Bibkey:
Cite (ACL):
Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, and Xiaodan Zhu. 2020. Dynamic Memory Induction Networks for Few-Shot Text Classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1087–1094, Online. Association for Computational Linguistics.
Cite (Informal):
Dynamic Memory Induction Networks for Few-Shot Text Classification (Geng et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.102.pdf
Video:
 http://slideslive.com/38928941