  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 effectively exploit
external (side) information. In this work, we demonstrate the effectiveness of
different attention based neural models that can directly exploit side
information available in technical documents or verified forums (e.g., research
publications on COVID-19 or WHO website). Augmented with a temperature
mechanism, the attention based neural models can selectively determine the
relevance of side information for a given user question, while ranking answers.
