Powering COVID-19 community Q&A with Curated Side Information
Manisha Verma, Kapil Thadani, and Shaunak Mishra

TL;DR
This paper proposes attention-based neural models that leverage external COVID-19 information sources to improve answer ranking on community Q&A platforms, addressing limitations of existing models in utilizing side information.
Contribution
It introduces novel attention mechanisms with a temperature control to effectively incorporate external COVID-19 resources into answer ranking models.
Findings
Attention-based models outperform baseline answer ranking methods.
Incorporating external COVID-19 information improves answer relevance.
Temperature mechanism enhances the model's ability to select relevant side information.
Abstract
Community question answering and discussion platforms such as Reddit, Yahoo! answers or Quora provide users the flexibility of asking open ended questions to a large audience, and replies to such questions maybe useful both to the user and the community on certain topics such as health, sports or finance. Given the recent events around COVID-19, some of these platforms have attracted 2000+ questions from users about several aspects associated with the disease. Given the impact of this disease on general public, in this work we investigate ways to improve the ranking of user generated answers on COVID-19. We specifically explore the utility of external technical sources of side information (such as CDC guidelines or WHO FAQs) in improving answer ranking on such platforms. We found that ranking user answers based on question-answer similarity is not sufficient, and existing models cannot…
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Taxonomy
TopicsExpert finding and Q&A systems · Topic Modeling · Misinformation and Its Impacts
