【ACL2021 findings】Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification 这篇主要核心是挖掘class的语义,把class加到输入上面,去引导样本的特征表示。尤其是样本很少的情况下,样本很难学习到明确的class语义,容易对一些细粒度的类别(比如意图)产生混淆:比如两句话:北京...
paper:Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification 录取:ACL2021 Findings 重新编辑:zenRRan 这篇主要核心是挖掘class的语义,把class加到输入上面,去引导样本的特征表示。尤其是样本很少的情况下,样本很难学习到明确的class语义,容易对一些细粒度的类别(比如意图...
Firstly, the semantic similarity modules of text-text and text-label are adopted for further merging to improve the feature extraction ability. Then, we introduce DLSC, an inter-class differences and label semantics contrastive loss that facilitates instance embeddings to approximate correct label ...
4)使用合成数据集训练其他语义分割方法,并在真实图像上进行测试 在本文中作者称之为“Few shot semantic segmentation methods”,并用DDPM的生成器替换了StyleGAN生成器进行了第二组实验。 所以这个大表能看出如下结论: 1)DDPM有着最好的语义分割结果;MAE紧随其后; 2)SwAV性能不佳:用判别式的方法进行预训练会压缩...
We study the problem of few shot learning for named entity recognition. Specifically, we leverage the semantic information in the names of the labels as a way of giving the model additional signal and enriched priors. We propose a neural architecture that consists of two BERT encoders, one to...
该部分用于预测每个token具体的标签,在few-shot场景下,并非是直接喂入MLP,而是与support set给定的标签的相似度进行预测。因此借助Tap-Net,提出L-TapNet,相比TapNet,L-TapNet融合了label name semantic和prototype。 (1)TapNet并非像prototype那样对所有support set的example根据类别进行平均,而是初始化了一个per...
Inspired by the above works, we propose a Few-shot Incremental learning method for Label-to-Image Transla- tion (FILIT). It enables a pre-trained translation model to learn novel semantic classes from a few samples incre- mentally (Fig. 1). To achieve this, we ...
基于了官方的bank 分类场景,里边使用了semantic_similarity 处理,上边对于text-embedding-ada-002 的配置就比较重要,否则会有提示找不到embedding 模型的问题 banking 分类处理 示例数据下载,对于test.csv 为了测试我删除了部分数据(标签数据),同时修改名称为testv3.csv ...
the representations learned by the state-of-the-art DDPMs and shows that they capture high-level semantic information valuable for downstream vision tasks. We design a simple semantic segmentation approach that exploits these representations and outperforms the alternatives in the few-shot operating ...
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