Knowledge Commons of Institute of Automation,CAS
Knowledge Guided Metric Learning for Few-Shot Text Classification | |
Dianbo Sui1,2![]() ![]() ![]() ![]() ![]() | |
2021-06 | |
会议名称 | Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies |
会议日期 | 2021-6 |
会议地点 | Online |
摘要 | 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. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48933 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Beijing Unisound Information Technology Co., Ltd. |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Dianbo Sui,Yubo Chen,Binjie Mao,et al. Knowledge Guided Metric Learning for Few-Shot Text Classification[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2021.naacl-main.261.(401KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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