论文标题
咳嗽：COVID-19 FAQ检索的挑战数据集和模型
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
论文作者
论文摘要
我们提出了一个大型的，具有挑战性的数据集，咳嗽，用于COVID-19 FAQ检索。类似于标准常见问题解答数据集，咳嗽包括三个部分：常见问题银行，查询银行和相关性集。 FAQ银行包含〜16K FAQ项目，这些物品从55个可靠的网站（例如CDC和WHO）中删除。为了进行评估，我们介绍了查询银行和相关性集，前者包含1,236个人参数的查询，而后者则包含每个查询的32个人类通知项目。我们通过测试基于BM25和BERT的不同常见问题检索模型来分析咳嗽，其中最佳模型在P@5下实现了48.8，这表明咳嗽带来了巨大的挑战，并鼓励将来的研究进一步改进。我们的咳嗽数据集可从https://github.com/sunlab-osu/covid-faq获得。
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.
