In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12 . 
 Question: What is used a baseline?
As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12

Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. 
 Question: How big is dataset of car-speak language?
$3,209$ reviews 

We compare our HR-VAE model with three strong baselines using VAE for text modelling:

VAE-LSTM-base: A variational autoencoder model which uses LSTM for both encoder and decoder. KL annealing is used to tackled the latent variable collapse issue BIBREF0;

VAE-CNN: A variational autoencoder model with a LSTM encoder and a dilated CNN decoder BIBREF7;

vMF-VAE: A variational autoencoder model using LSTM for both encoder and decoder where the prior distribution is the von Mises-Fisher (vMF) distribution rather than a Gaussian distribution BIBREF5. 
 Question: Do they compare against state of the art text generation?

Yes