Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products

Ohad Rozen, David Carmel, Avihai Mejer, Vitaly Mirkis, Yftah Ziser


Abstract
Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention. Answering subjective and opinion-based questions is most challenging due to the dependency on customer generated content. Previous works mostly focused on review-aware answer prediction; however, these approaches fail for new or unpopular products, having no (or only a few) reviews at hand. In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products. We measure the contextual similarity between products based on the answers they provide for the same question. A mixture-of-expert framework is used to predict the answer by aggregating the answers from contextually similar products. Empirical results demonstrate that our model outperforms strong baselines on some segments of questions, namely those that have roughly ten or more similar resolved questions in the corpus. We additionally publish two large-scale datasets used in this work, one is of similar product question pairs, and the second is of product question-answer pairs.
Anthology ID:
2021.naacl-main.23
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
242–253
Language:
URL:
https://aclanthology.org/2021.naacl-main.23
DOI:
10.18653/v1/2021.naacl-main.23
Bibkey:
Cite (ACL):
Ohad Rozen, David Carmel, Avihai Mejer, Vitaly Mirkis, and Yftah Ziser. 2021. Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 242–253, Online. Association for Computational Linguistics.
Cite (Informal):
Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products (Rozen et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.23.pdf
Video:
 https://aclanthology.org/2021.naacl-main.23.mp4
Data
Amazon-PQA