@inproceedings{zhang-etal-2021-cough,
title = "{COUGH}: A Challenge Dataset and Models for {COVID}-19 {FAQ} Retrieval",
author = "Zhang, Xinliang Frederick and
Sun, Heming and
Yue, Xiang and
Lin, Simon and
Sun, Huan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.305",
doi = "10.18653/v1/2021.emnlp-main.305",
pages = "3759--3769",
abstract = "We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set. The FAQ Bank contains {\textasciitilde}16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1,236 human-paraphrased queries while the latter contains {\textasciitilde}32 human-annotated FAQ items for each query. We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement. Our COUGH dataset is available at \url{https://github.com/sunlab-osu/covid-faq}.",
}
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<abstract>We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1,236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query. We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement. Our COUGH dataset is available at https://github.com/sunlab-osu/covid-faq.</abstract>
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%0 Conference Proceedings
%T COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
%A Zhang, Xinliang Frederick
%A Sun, Heming
%A Yue, Xiang
%A Lin, Simon
%A Sun, Huan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F zhang-etal-2021-cough
%X We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1,236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query. We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement. Our COUGH dataset is available at https://github.com/sunlab-osu/covid-faq.
%R 10.18653/v1/2021.emnlp-main.305
%U https://aclanthology.org/2021.emnlp-main.305
%U https://doi.org/10.18653/v1/2021.emnlp-main.305
%P 3759-3769
Markdown (Informal)
[COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval](https://aclanthology.org/2021.emnlp-main.305) (Zhang et al., EMNLP 2021)
ACL
- Xinliang Frederick Zhang, Heming Sun, Xiang Yue, Simon Lin, and Huan Sun. 2021. COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3759–3769, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.