2020
EMNLP
EMNLP 2020
Using the Poly-encoder for a COVID-19 Question Answering System
Abstract
AbstractTo 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.
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
Authors
Topics
Natural Language Processing > Generation > Dialogue Systems
Natural Language Processing > Applications > Information Retrieval
Natural Language Processing > Applications > Question Answering
Artificial Intelligence > Core AI > Information Retrieval
Deep Learning > Learning Types > Retrieval-Augmented Generation