2020/10/24 by Xinliang Frederick Zhang, Zhang, Xinliang Frederick, Heming Sun +7 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Natural Language Processing Techniques #Text and Document Classification Technologies #Topic Modeling #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.2010.12800
EMNLP'21 Main Conference
openalex publication_date 2020/10/24 · arxiv created 2021/09/10 · arxiv updated 2021/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.