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        <title>Dynamic Memory Induction Networks for Few-Shot Text Classification</title>
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        <namePart type="given">Ruiying</namePart>
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        <namePart type="given">Binhua</namePart>
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        <namePart type="given">Xiaodan</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Dynamic Memory Induction Networks for Few-Shot Text Classification
%A Geng, Ruiying
%A Li, Binhua
%A Li, Yongbin
%A Sun, Jian
%A Zhu, Xiaodan
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F geng-etal-2020-dynamic
%X 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.
%R 10.18653/v1/2020.acl-main.102
%U https://aclanthology.org/2020.acl-main.102
%U https://doi.org/10.18653/v1/2020.acl-main.102
%P 1087-1094

Markdown (Informal)

[Dynamic Memory Induction Networks for Few-Shot Text Classification](https://aclanthology.org/2020.acl-main.102) (Geng et al., ACL 2020)

• Dynamic Memory Induction Networks for Few-Shot Text Classification (Geng et al., ACL 2020)

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.