What Are People Asking About COVID-19? A Question Classification Dataset

We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple sources were not answered by any FAQ websites of reputable organizations such as the CDC and FDA... (read more)

PDF Abstract ACL 2020 PDF ACL 2020 Abstract

Tasks


Datasets


Introduced in the Paper:

COVID-Q

Mentioned in the Paper:

NAIST COVID

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
Triplet Loss
Loss Functions
Weight Decay
Regularization
Softmax
Output Functions
Adam
Stochastic Optimization
Multi-Head Attention
Attention Modules
Dropout
Regularization
GELU
Activation Functions
Attention Dropout
Regularization
Linear Warmup With Linear Decay
Learning Rate Schedules
Dense Connections
Feedforward Networks
Layer Normalization
Normalization
Scaled Dot-Product Attention
Attention Mechanisms
WordPiece
Subword Segmentation
Residual Connection
Skip Connections
BERT
Language Models