AnswerFact: Fact Checking in Product Question Answering

Wenxuan Zhang, Yang Deng, Jing Ma, Wai Lam


Abstract
Product-related question answering platforms nowadays are widely employed in many E-commerce sites, providing a convenient way for potential customers to address their concerns during online shopping. However, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information, which may even cause a commercial loss in E-commerce business. To tackle this issue, we investigate to predict the veracity of answers in this paper and introduce AnswerFact, a large scale fact checking dataset from product question answering forums. Each answer is accompanied by its veracity label and associated evidence sentences, providing a valuable testbed for evidence-based fact checking tasks in QA settings. We further propose a novel neural model with tailored evidence ranking components to handle the concerned answer veracity prediction problem. Extensive experiments are conducted with our proposed model and various existing fact checking methods, showing that our method outperforms all baselines on this task.
Anthology ID:
2020.emnlp-main.188
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2407–2417
Language:
URL:
https://aclanthology.org/2020.emnlp-main.188
DOI:
10.18653/v1/2020.emnlp-main.188
Bibkey:
Cite (ACL):
Wenxuan Zhang, Yang Deng, Jing Ma, and Wai Lam. 2020. AnswerFact: Fact Checking in Product Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2407–2417, Online. Association for Computational Linguistics.
Cite (Informal):
AnswerFact: Fact Checking in Product Question Answering (Zhang et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.188.pdf