Automatic Validation of Textual Attribute Values in ECommerce Catalog by Learning with Limited Labeled Data
Yaqing Wang: University at Buffalo SUNY ; Yifan Ethan Xu: Amazon.com; Xian Li: Amazon.com; Xin Luna Dong: Amazon.com; Jing Gao: University at Buffalo
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. It is very important to validate the correctness of these values in order to improve shopper experiences and enable more effective product recommendation. Due to the huge volume of products, an effective automatic validation approach is needed. In this paper, we propose to develop an automatic validation approach that verifies the correctness of textual attribute values for products. This can be formulated as a task as cross-checking a textual attribute value against product profile, which is a short textual description of the product on eCommerce website. 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. Due to the category difference, annotation has to be done on all the categories, which is impossible to achieve in real practice.
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. With this model, annotation costs can be significantly reduced as we make best use of labeled data from limited 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 different records 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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