# COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval

arXiv: https://arxiv.org/abs/2010.12800 · HTML: https://mlsanity.com/paper/2010.12800/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 10 of 10)

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- [Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains](https://mlsanity.com/paper/2201.11153/) (2022)
- [MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval](https://mlsanity.com/paper/2302.11953/) (2023)
- [Regularized Contrastive Learning of Semantic Search](https://mlsanity.com/paper/2209.13241/) (2022)
- [Portuguese FAQ for Financial Services](https://mlsanity.com/paper/2311.11331/) (2023)
- [ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese](https://mlsanity.com/paper/2504.21017/) (2025)
- [Nepali Passport Question Answering: A Low-Resource Dataset for Public Service Applications](https://mlsanity.com/paper/2603.13320/) (2026)

## Abstract (arXiv)

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.