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Learning to Learn Kernels with Variational Random Features
In this work, we introduce kernels with random Fourier features in the m...
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Few-shot acoustic event detection via meta-learning
We study few-shot acoustic event detection (AED) in this paper. Few-shot...
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Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation
The field of few-shot learning has been laboriously explored in the supe...
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Semi Few-Shot Attribute Translation
Recent studies have shown remarkable success in image-to-image translati...
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OpenTag: Open Attribute Value Extraction from Product Profiles
Extraction of missing attribute values is to find values describing an a...
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Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
We investigate the problem of reliably assessing group fairness when lab...
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An interpretable latent variable model for attribute applicability in the Amazon catalogue
Learning attribute applicability of products in the Amazon catalog (e.g....
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Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data
Product catalogs are valuable resources for eCommerce website. In the catalog, a product is associated with multiple attributes whose values are short texts, such as product name, brand, functionality and flavor. Usually individual retailers self-report these key values, and thus the catalog information unavoidably contains noisy facts. Although existing deep neural network models have shown success in conducting cross-checking between two pieces of texts, their success has to be dependent upon a large set of quality labeled data, which are hard to obtain in this validation task: products span a variety of categories. To address the aforementioned challenges, we propose a novel meta-learning latent variable approach, called MetaBridge, which can learn transferable knowledge from a subset of categories with limited labeled data and capture the uncertainty of never-seen categories with unlabeled data. More specifically, we make the following contributions. (1) We formalize the problem of validating the textual attribute values of products from a variety of categories as a natural language inference task in the few-shot learning setting, and propose a meta-learning latent variable model to jointly process the signals obtained from product profiles and textual attribute values. (2) We propose to integrate meta learning and latent variable in a unified model to effectively capture the uncertainty of various categories. (3) We propose a novel objective function based on latent variable model in the few-shot learning setting, which ensures distribution consistency between unlabeled and labeled data and prevents overfitting by sampling from the learned distribution. Extensive experiments on real eCommerce datasets from hundreds of categories demonstrate the effectiveness of MetaBridge on textual attribute validation and its outstanding performance compared with state-of-the-art approaches.
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