zero-shot learning/one-shot learning/few-shot learning 都是meta learning领域的一个分支,meta learning在计算机视觉领域的研究相对成熟,但是在NLP领域,尚处于发展阶段。 笑个不停:few-shot learning/one-shot learning 小样本/零样本学习 学习笔记(持续更新)55 赞同 · 3 评论文章 本人在调研小样本文本分类的过...
Multiway Attention Networks论文解读 宋原青发表于NLP论文... 论文:Jumping Knowledge Networks GEETEST极验 小样本(few-shot)学习--《Matching Networks for One Shot Learning》论文解读 丹尼尔小博...发表于小样本学习...打开知乎App 在「我的页」右上角打开扫一扫 其他扫码方式:微信 下载知乎App 开通机构号 无...
Afterward, we test our QA model by performing few-shot learning experiments on multiple pre-trained language models of different sizes that range from the DistilBERT to the RoBERTa-large. We are surprised to find that even the DistilBERT, which is the smallest language model we tested with ...
this paper studies text classification problems under the condition of few labelled samples and proposes a few-shot short-text classification method (Meta-FCS) that combines the advantages of text semantic vector representation, meta-learning, fine-tuning and vector similarity...
在文本分类中,经常碰到一些很少出现过的类别或这样不均衡的类别样本,而且当前的few-shot技术经常会将输入的query和support的样本集合进行sample-wise级别的对比。但是,如果跟同一个类别下的不同表达的样本去对比的时候产生的效果就不太好。 因此,文章的作者就提出了,通过学习sample所属于的类别的表示得到class-wise的向...
这篇笔记总结了这篇论文的主要思路,Few-Shot Text Classification with Distributional Signatures - ICLR 2020。 论文链接:https://arxiv.org/abs/1908.06039 论文代码链接:https://github.com/YujiaBao/Distributional-Signatures 笔记链接(完结): -第1篇:要解决的问题,以及训练过程是什么样子的 ...
图1:有一个查询样本的C-way K-shot (C = 3, K = 2)问题的归纳网络架构 4.1 编码器模块 该模块是具有自注意力的双向递归神经网络,如[Lin 等人,2017b]所示。 给定输入文本 ,由一系列字嵌入(word embeddings)表示。 我们使用双向LSTM来处理文本: ...
Few-shot text classification targets at the situation where a model is developed to classify newly incoming query instances after acquiring knowledge from a few support instances. In this paper, we investigate few-shot text classification under a metric-based meta-learning framework. While the ...
论文解读:Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference 随着GPT-3模型的提出,现在的预训练模型形成了一个全新的无监督范式——引入task description (demostration)。本文,我们引入Pattern Exploiting Training (PET)方法,将输入样本转换为一种完形填空(cloze-style...
The proposed modified hierarchical pooling method exhibits significant classification performance in the few-shot transfer learning tasks compared with other alternative methods. 展开 关键词: Task analysis Data models Training Computational modeling Text categorization Deep learning Feature extraction ...