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

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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.

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APA

Zhang, X. F., Sun, H., Yue, X., Lin, S., & Sun, H. (2021). COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval. In EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 3759–3769). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.emnlp-main.305

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