Powering COVID-19 community Q&A with Curated Side Information
This work addresses the challenge of providing reliable information to the general public on COVID-19 through community platforms, but it is incremental as it builds on existing attention-based methods for a specific domain.
The paper tackled the problem of ranking user-generated answers on COVID-19 Q&A platforms by exploring the use of external technical sources like CDC guidelines and WHO FAQs, and found that attention-based neural models with a temperature mechanism improved answer ranking by selectively determining the relevance of side information.
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.