Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction

Keiji Shinzato, Naoki Yoshinaga, Yandi Xia, Wei-Te Chen


Abstract
A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products. Although this challenge is partially addressed by a question answering (QA) approach which finds a value in product data for a given query (attribute), it does not work effectively for rare and ambiguous queries. We thus propose simple knowledge-driven query expansion based on possible answers (values) of a query (attribute) for QA-based AVE. We retrieve values of a query (attribute) from the training data to expand the query. We train a model with two tricks, knowledge dropout and knowledge token mixing, which mimic the imperfection of the value knowledge in testing. Experimental results on our cleaned version of AliExpress dataset show that our method improves the performance of AVE (+6.08 macro F1), especially for rare and ambiguous attributes (+7.82 and +6.86 macro F1, respectively).
Anthology ID:
2022.acl-short.25
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
227–234
Language:
URL:
https://aclanthology.org/2022.acl-short.25
DOI:
10.18653/v1/2022.acl-short.25
Bibkey:
Cite (ACL):
Keiji Shinzato, Naoki Yoshinaga, Yandi Xia, and Wei-Te Chen. 2022. Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 227–234, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction (Shinzato et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-short.25.pdf