@inproceedings{chen-etal-2023-generate,
title = "Generate-then-Retrieve: Intent-Aware {FAQ} Retrieval in Product Search",
author = "Chen, Zhiyu and
Choi, Jason and
Fetahu, Besnik and
Rokhlenko, Oleg and
Malmasi, Shervin",
editor = "Sitaram, Sunayana and
Beigman Klebanov, Beata and
Williams, Jason D",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-industry.73",
doi = "10.18653/v1/2023.acl-industry.73",
pages = "763--771",
abstract = "Frequently Asked Question (FAQ) retrieval aims at retrieving question-answer pairs for a given a user query. Integrating FAQ retrieval with product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support. Providing FAQ content without disrupting user{'}s shopping experience poses challenges on deciding when and how to show FAQ results. Our proposed intent-aware FAQ retrieval consists of (1) an intent classifier that predicts whether the query is looking for an FAQ; (2) a reformulation model that rewrites query into a natural question. Offline evaluation demonstrates that our approach improves 12{\%} in Hit@1 on retrieving ground-truth FAQs, while reducing latency by 95{\%} compared to baseline systems. These improvements are further validated by real user feedback, where more than 99{\%} of users consider FAQs displayed on top of product search results is helpful. Overall, our findings show promising directions for integrating FAQ retrieval into product search at scale.",
}
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<abstract>Frequently Asked Question (FAQ) retrieval aims at retrieving question-answer pairs for a given a user query. Integrating FAQ retrieval with product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support. Providing FAQ content without disrupting user’s shopping experience poses challenges on deciding when and how to show FAQ results. Our proposed intent-aware FAQ retrieval consists of (1) an intent classifier that predicts whether the query is looking for an FAQ; (2) a reformulation model that rewrites query into a natural question. Offline evaluation demonstrates that our approach improves 12% in Hit@1 on retrieving ground-truth FAQs, while reducing latency by 95% compared to baseline systems. These improvements are further validated by real user feedback, where more than 99% of users consider FAQs displayed on top of product search results is helpful. Overall, our findings show promising directions for integrating FAQ retrieval into product search at scale.</abstract>
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%0 Conference Proceedings
%T Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search
%A Chen, Zhiyu
%A Choi, Jason
%A Fetahu, Besnik
%A Rokhlenko, Oleg
%A Malmasi, Shervin
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F chen-etal-2023-generate
%X Frequently Asked Question (FAQ) retrieval aims at retrieving question-answer pairs for a given a user query. Integrating FAQ retrieval with product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support. Providing FAQ content without disrupting user’s shopping experience poses challenges on deciding when and how to show FAQ results. Our proposed intent-aware FAQ retrieval consists of (1) an intent classifier that predicts whether the query is looking for an FAQ; (2) a reformulation model that rewrites query into a natural question. Offline evaluation demonstrates that our approach improves 12% in Hit@1 on retrieving ground-truth FAQs, while reducing latency by 95% compared to baseline systems. These improvements are further validated by real user feedback, where more than 99% of users consider FAQs displayed on top of product search results is helpful. Overall, our findings show promising directions for integrating FAQ retrieval into product search at scale.
%R 10.18653/v1/2023.acl-industry.73
%U https://aclanthology.org/2023.acl-industry.73
%U https://doi.org/10.18653/v1/2023.acl-industry.73
%P 763-771
Markdown (Informal)
[Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search](https://aclanthology.org/2023.acl-industry.73) (Chen et al., ACL 2023)
ACL
- Zhiyu Chen, Jason Choi, Besnik Fetahu, Oleg Rokhlenko, and Shervin Malmasi. 2023. Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 763–771, Toronto, Canada. Association for Computational Linguistics.