A Unified Generative Approach to Product Attribute-Value Identification

• URL: http://arxiv.org/abs/2306.05605v1
• Date: Fri, 9 Jun 2023 00:33:30 GMT
• Title: A Unified Generative Approach to Product Attribute-Value Identification
• Authors: Keiji Shinzato, Naoki Yoshinaga, Yandi Xia and Wei-Te Chen
• Abstract summary: We explore a generative approach to the product attribute-value identification (PAVI) task. We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text. Experimental results confirm that our generation-based approach outperforms the existing extraction and classification-based methods.
• Score: 6.752749933406399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Product attribute-value identification (PAVI) has been studied to link products on e-commerce sites with their attribute values (e.g., <Material, Cotton>) using product text as clues. Technical demands from real-world e-commerce platforms require PAVI methods to handle unseen values, multi-attribute values, and canonicalized values, which are only partly addressed in existing extraction- and classification-based approaches. Motivated by this, we explore a generative approach to the PAVI task. We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text. Since the attribute value pairs are unordered set elements, how to linearize them will matter; we, thus, explore methods of composing an attribute-value pair and ordering the pairs for the task. Experimental results confirm that our generation-based approach outperforms the existing extraction and classification-based methods on large-scale real-world datasets meant for those methods.

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