2.1 Problem Formulation 本文将有大量标注数据的意图称为seen intent,表示为Y_s,有少量标注数据的意图为novel intent,表示为Y_n,二者交集为空。seen intent集表示为: novel intent集表示为: FSID的目标为: GFSID不同于FSID,其目标是既能分辨seen intent,也能分辨novel intent,其目标为: 其中,Dj是Dn和Ds的并集。
Few-shot intent detection is a practical challenge task,because new intents are frequently emerging and collecting large-scale data for them could be costly.Meta-learning,a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks,has been a popular way ...
上面这两个公式中 Cintenti 和Csloti 表示意图原型和槽原型(prototype),原型是支持集(support set)中所有样本的嵌入向量取平均计算得到的,Eslot() 和Eintent() 是计算嵌入向量的公式,作者使用 BERT 计算的嵌入向量,其中意图的嵌入向量是把句子中 token 的嵌入向量取均值,SIM 是点积相似度计算。 2.2 原型融合 ...
AugFPN: Improving Multi-scale Feature Learning for Object Detection AugFPN: Improving Multi-scale Feature Learning for Object Detection 写的第一篇博文,读过的文章 欢迎讨论 论文地址:arxiv.org/abs/1912.05384 由中科院与地平线在19年发表,提出AugFPN结构,可替换FPN,检测效果得到提升。 文章首先分析FPN模型中...
In this section, we introduce a novel dialogue understanding model that jointly learns the intent detection and slot filling tasks, and employs the trust gating mechanism to reduce the noise sharing problem in the few-shot setting (Sect. 4.1). To help the model generalize better to unseen few...
The source code of paper "Self-Supervised Task Augmentation for Few-Shot Intent Detection" - bbsngg/STAM
Few-shot Learning for Multi-label Intent Detection Yutai Hou,Yongkui Lai,Yushan Wu,... - 2020 - 被引量: 0 Few-shot partial multi-label learning with synthetic features network Sun Yifan,Zhao Yunfeng,Yu ...
论文名称:Few-shot Learning for Multi-label Intent Detection 推荐一篇来自哈尔滨工业大学赛尔实验室刘挺教授组工作,此项工作研究了用于用户意图检测的少样本多标签分类方法。刘挺教授现任哈工大计算学部主任...
[ICCV 2019] Bidirectional One-Shot Unsupervised Domain Mapping [ICML 2019] Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning [ICCV 2019] Deep Meta Learning for Real-Time Target-Aware Visual Tracking Releases No releases published...
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification 方向:对话系统,未知意图检测 问题:在许多现实场景中,用户意图可能随着时间的推移而频繁变化,未知意图检测已成为一个重要问题。 方法:语义增强高斯混合模型。