@inproceedings{shinzato-etal-2023-unified,
    title = "A Unified Generative Approach to Product Attribute-Value Identification",
    author = "Shinzato, Keiji  and
      Yoshinaga, Naoki  and
      Xia, Yandi  and
      Chen, Wei-Te",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.413",
    doi = "10.18653/v1/2023.findings-acl.413",
    pages = "6599--6612",
    abstract = "Product attribute-value identification (PAVI) has been studied to link products on e-commerce sites with their attribute values (e.g., â¨Material, Cottonâ©) 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.",
}

Product attribute-value identification (PAVI) has been studied to link products on e-commerce sites with their attribute values (e.g., â¨Material, Cottonâ©) 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.
Shinzato, Keiji Yoshinaga, Naoki Xia, Y i Chen, Wei-Te
A Unified Generative Approach to Product Attribute-Value Identification