@inproceedings{geng-etal-2020-dynamic,
    title = "Dynamic Memory Induction Networks for Few-Shot Text Classification",
    author = "Geng, Ruiying  and
      Li, Binhua  and
      Li, Yongbin  and
      Sun, Jian  and
      Zhu, Xiaodan",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.acl-main.102/",
    doi = "10.18653/v1/2020.acl-main.102",
    pages = "1087--1094",
    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."
}