Towards Translating Objective Product Attributes Into Customer Language

Ram Yazdi, Oren Kalinsky, Alexander Libov, Dafna Shahaf

Abstract
When customers search online for a product they are not familiar with, their needs are often expressed through subjective product attributes, such as ”picture quality” for a TV or ”easy to clean” for a sofa. In contrast, the product catalog in online stores includes objective attributes such as ”screen resolution” or ”material”. In this work, we aim to find a link between the objective product catalog and the subjective needs of the customers, to help customers better understand the product space using their own words. We apply correlation-based methods to the store’s product catalog and product reviews in order to find the best potential links between objective and subjective attributes; next, Large Language Models (LLMs) reduce spurious correlations by incorporating common sense and world knowledge (e.g., picture quality is indeed affected by screen resolution, and 8k is the best one). We curate a dataset for this task and show that our combined approach outperforms correlation-only and causation-only approaches.
Anthology ID:
2024.naacl-industry.20
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Yi Yang, Aida Davani, Avi Sil, Anoop Kumar
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
239–247
Language:
URL:
https://aclanthology.org/2024.naacl-industry.20
DOI:
Bibkey:
Cite (ACL):
Ram Yazdi, Oren Kalinsky, Alexander Libov, and Dafna Shahaf. 2024. Towards Translating Objective Product Attributes Into Customer Language. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 239–247, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Towards Translating Objective Product Attributes Into Customer Language (Yazdi et al., NAACL 2024)
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