Poly encoder based Task Adaptation for COVID 19 Question Answering System
In this unprecedented era of COVID 19, there is a huge information need for truthful answers to questions relating to all aspects of the Coronavirus. In this paper, we adapt the PolyEncoder (Humeau et al. 2020) informational retrieval from FAQs. We show that after finetuning the Poly encoder we achieve higher recall at the same level of precision. We compare several models on the task of answer or question selection on evaluation sets and demonstrate superior performance. We make our code publicly available for other researchers to use.