Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction

Yifan Ding, Yan Liang, Nasser Zalmout, Xian Li, Christan Grant, Tim Weninger


Abstract
The product attribute value extraction (AVE) task aims to capture key factual information from product profiles, and is useful for several downstream applications in e-Commerce platforms. Previous contributions usually formulate this task using sequence labeling or reading comprehension architectures. However, sequence labeling models tend to be conservative in their predictions resulting in a high false negative rate. Existing reading comprehension formulations, on the other hand, can over-generate attribute values which hinders precision. In the present work we address these limitations with a new end-to-end pipeline framework called Ask-and-Verify. Given a product and an attribute query, the Ask step detects the top-K span candidates (i.e. possible attribute values) from the product profiles, then the Verify step filters out false positive candidates. We evaluate Ask-and-Verify model on Amazon’s product pages and AliExpress public dataset, and present a comparative analysis as well as a detailed ablation study. Despite its simplicity, we show that Ask-and-Verify outperforms recent state-of-the-art models by up to 3.1% F1 absolute improvement points, while also scaling to thousands of attributes.
Anthology ID:
2022.emnlp-industry.9
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Editors:
Yunyao Li, Angeliki Lazaridou
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–110
Language:
URL:
https://aclanthology.org/2022.emnlp-industry.9
DOI:
10.18653/v1/2022.emnlp-industry.9
Bibkey:
Cite (ACL):
Yifan Ding, Yan Liang, Nasser Zalmout, Xian Li, Christan Grant, and Tim Weninger. 2022. Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 110–110, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction (Ding et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-industry.9.pdf