Exploring Generative Models for Joint Attribute Value Extraction from Product Titles

• URL: http://arxiv.org/abs/2208.07130v1
• Date: Mon, 15 Aug 2022 11:51:31 GMT
• Title: Exploring Generative Models for Joint Attribute Value Extraction from Product Titles
• Authors: Kalyani Roy, Tapas Nayak and Pawan Goyal
• Abstract summary: Attribute Value Extraction (AVE) deals with extracting the attributes of a product and their values from its title or description. We present two types of generative paradigms, namely, word sequence-based and positional sequence-based, by formulating the AVE task as a generation problem. We conduct experiments on two datasets where the generative approaches achieve the new state-of-the-art results.
• Score: 11.444095166873325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attribute values of the products are an essential component in any e-commerce platform. Attribute Value Extraction (AVE) deals with extracting the attributes of a product and their values from its title or description. In this paper, we propose to tackle the AVE task using generative frameworks. We present two types of generative paradigms, namely, word sequence-based and positional sequence-based, by formulating the AVE task as a generation problem. We conduct experiments on two datasets where the generative approaches achieve the new state-of-the-art results. This shows that we can use the proposed framework for AVE tasks without additional tagging or task-specific model design.

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