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。
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 ...
Few-shot out-of-distribution (OOD) intent detection aims to detect in-distribution (ID) intents and reject OOD intents with few ID training data. This data scarcity often causes traditional OOD intent detection models to rely on spurious correlations, leading to attention misallocation, where ...
,可以用simese Network解决few shot learning 问题 两种训练Simese Network的两种方法: 第一种方法: 1数据整理: 将数据集分为正样本和负样本,打好标签。 例子:每次从训练集中随机抽样构建正负样本: 2.特征提取: 要训练的神经网络: 正样本实例: 负样本示例: 训练完的模型可以用来做one (few)shot prediction 第...
The source code of paper "Self-Supervised Task Augmentation for Few-Shot Intent Detection" - bbsngg/STAM
Few-shot transfer learning for individualized braking intent detection on neuromorphic hardware Nathan Lutes,Venkata Sriram Si...,K Krishnamurthy - 《Pattern Recognition》 - 2025 - 被引量: 0 Dual Class Knowledge...
The code of AAAI2021 paperFew-Shot Learning for Multi-label Intent Detection. The code framework is based on few-shot learning platform:MetaDialog. Get Started Requirement python >= 3.6 pytorch >= 1.5.0 transformers >= 2.8.0 allennlp >= 0.8.2 tqdm >= 4.33.0 ...
Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dia
we introduce Intent-driven In-context Learning for Few-shot DST (IDIC-DST). By extracting user's intent, we propose an Intent-driven Dialogue Information Augmentation module to augment the dialogue information, which can track dialogue states more effectively. Moreover, we mask noisy information fr...
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging out... Y Bai,Z Han,C Zhang,... - IEEE 被引量: 0发表: 2023年 Few-shot out-of-scope intent classification: analyzing the robustness of prompt-...