Collecting Verified COVID-19 Question Answer Pairs

Poliak, Adam; Fleming, Max; Costello, Cash; Murray, Kenton; Yarmohammadi, Mahsa; Pandya, Shivani R.; Irani, Darius; Agarwal, Milind; Sharma, Udit; Sun, Shuo; Ivanov, N. V.; Shang, Lingxi; Srinivasan, Kaushik; Lee, Seolhwa; Han, Xu; Agarwal, Smisha; Sedoc, João

doi:10.18653/v1/2020.nlpcovid19-2.31

Cited by 14 publications

(14 citation statements)

References 2 publications

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“…Automating knowledge extraction of the latest information from reliable sources to automatically create question and answer pairs and update the chatbot’s knowledge base is a viable solution to this challenge. 38 , 39 …”

Section: Discussionmentioning

confidence: 99%

Chatbot use cases in the Covid-19 public health response

Amiri

Karahanna

2022

Journal of the American Medical Informatics Association

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Objective To identify chatbot use cases deployed for public health response activities during the Covid-19 pandemic. Material and Methods We searched PubMed/MEDLINE, Web of Knowledge, and Google Scholar in October 2020 and performed a follow-up search in July 2021. We screened articles based on their abstracts and keywords in their text, reviewed potentially relevant articles, and screened their references to (a) assess whether the article met inclusion criteria, and (b) identify additional articles. Chatbots, their use cases and chatbot design characteristics were extracted from the articles and information from other sources and by accessing those chatbots that were publicly accessible. Results Our search returned 3334 articles, 61 articles met our inclusion criteria, and 61 chatbots deployed in 30 countries were identified. We categorized chatbots based on their public health response use case(s) and design. Six categories of public health response use cases emerged comprising 15 distinct use cases: risk assessment, information dissemination, surveillance, post-covid eligibility screening, distributed coordination, and vaccine scheduler. Design-wise, chatbots were relatively simple, implemented using decision-tree structures and predetermined response options, and focused on a narrow set of simple tasks, presumably due to need for quick deployment. Conclusion Chatbots’ scalability, wide accessibility, ease of use, and fast information dissemination provide complementary functionality that augments public health workers in public health response activities, addressing capacity constraints, social-distancing requirements, and misinformation. Additional use cases, more sophisticated chatbot designs, and opportunities for synergies in chatbot development should be explored.

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Section: Discussionmentioning

confidence: 99%

Chatbot use cases in the Covid-19 public health response

Amiri

Karahanna

2022

Journal of the American Medical Informatics Association

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“…The specialized, closed-domain knowledge base of the system in its instantiation for this experiment consists of questions and answers related to COVID-19 that were extracted from two sources. The first batch was pulled from a project that scraped COVID-19 data from 40 trusted websites, created question and answer pairs based on this data, and employed health care experts to determine the relevance of said pairs to unanswered questions [29]. For our purposes, pairs with a rating below 90 were filtered out and duplicates were removed.…”

Section: Datasetsmentioning

confidence: 99%

Toward an eclectic and malleable multiagent educational assistant

Briel

2021

Comput Appl Eng Educ

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Conversational agents are systems capable of processing and responding to human language. They have evolved over the years from a means to pass the Turing Test to chatbots that fulfill a utilitarian purpose. Closed-domain chatbots are specialized in a specific knowledge base and are often used in an attempt to assist users in an educational context. Existing open-source, educational assistant chatbots are narrow in immediate functionality, thus lim-

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“…The most related works to ours are Sun and Sedoc (2020) and Poliak et al (2020), both of which constructed a collection of COVID-19 FAQs by scraping authoritative websites. However, the dataset in the former work is not available yet and the latter work does not evaluate models on their dataset, and there is still a great need to understand how existing models would perform on the COVID-19 FAQ retrieval task.…”

Section: Related Workmentioning

confidence: 99%

“…We developed scrapers 4 adapted from Poliak et al (2020), and add special features to COUGH dataset. Web scraping: We collect FAQ items from authoritative international organizations, state governments and other credible websites including reliable encyclopedias and medical forums.…”

Section: Faq Bank Constructionmentioning

confidence: 99%

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

Zhang¹,

Sun²,

Yue³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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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 humanannotated 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. * Work was done when the first two authors were at OSU. 1 q and a are question and answer fields in an FAQ item. question similarity and bert-based query-answer relevance. In

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Collecting Verified COVID-19 Question Answer Pairs

Cited by 14 publications

References 2 publications

Chatbot use cases in the Covid-19 public health response

Chatbot use cases in the Covid-19 public health response

Toward an eclectic and malleable multiagent educational assistant

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

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