@inproceedings{mass-etal-2020-unsupervised,
title = "Unsupervised {FAQ} Retrieval with Question Generation and {BERT}",
author = "Mass, Yosi and
Carmeli, Boaz and
Roitman, Haggai and
Konopnicki, David",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.74/",
doi = "10.18653/v1/2020.acl-main.74",
pages = "807--812",
abstract = "We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. The two models match user queries to FAQ answers and questions, respectively. We alleviate the missing labeled data of the latter by automatically generating high-quality question paraphrases. We show that our model is on par and even outperforms supervised models on existing datasets."
}
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%0 Conference Proceedings
%T Unsupervised FAQ Retrieval with Question Generation and BERT
%A Mass, Yosi
%A Carmeli, Boaz
%A Roitman, Haggai
%A Konopnicki, David
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F mass-etal-2020-unsupervised
%X We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. The two models match user queries to FAQ answers and questions, respectively. We alleviate the missing labeled data of the latter by automatically generating high-quality question paraphrases. We show that our model is on par and even outperforms supervised models on existing datasets.
%R 10.18653/v1/2020.acl-main.74
%U https://aclanthology.org/2020.acl-main.74/
%U https://doi.org/10.18653/v1/2020.acl-main.74
%P 807-812
Markdown (Informal)
[Unsupervised FAQ Retrieval with Question Generation and BERT](https://aclanthology.org/2020.acl-main.74/) (Mass et al., ACL 2020)
ACL