Automatic Validation of Textual Attribute Values in E-commerce Catalog
by Learning with Limited Labeled Data
- URL: http://arxiv.org/abs/2006.08779v3
- Date: Tue, 23 Jun 2020 03:52:03 GMT
- Title: Automatic Validation of Textual Attribute Values in E-commerce Catalog
by Learning with Limited Labeled Data
- Authors: Yaqing Wang, Yifan Ethan Xu, Xian Li, Xin Luna Dong and Jing Gao
- Abstract summary: We propose a novel meta-learning latent variable approach, called MetaBridge.
It can learn transferable knowledge from a subset of categories with limited labeled data.
It can capture the uncertainty of never-seen categories with unlabeled data.
- Score: 61.789797281676606
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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