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.- Anthology ID:
- 2023.findings-acl.413
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6599–6612
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.413
- DOI:
- 10.18653/v1/2023.findings-acl.413
- Cite (ACL):
- Keiji Shinzato, Naoki Yoshinaga, Yandi Xia, and Wei-Te Chen. 2023. A Unified Generative Approach to Product Attribute-Value Identification. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6599–6612, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Unified Generative Approach to Product Attribute-Value Identification (Shinzato et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.findings-acl.413.pdf