COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
Xinliang Frederick Zhang, Heming Sun, Xiang Yue, Simon Lin, Huan Sun
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.- Anthology ID:
- 2021.emnlp-main.305
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3759–3769
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.305
- DOI:
- 10.18653/v1/2021.emnlp-main.305
- Cite (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.
- Cite (Informal):
- COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval (Zhang et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.emnlp-main.305.pdf
- Code
- sunlab-osu/covid-faq
- Data
- COUGH