Using the Poly-encoder for a COVID-19 Question Answering System
Seolhwa Lee, João Sedoc · 2020
To combat misinformation regarding COVID-19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19.We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs.We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score.We make our code publicly available for other researchers to use.