TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: Yes

PROBLEM: We train MaLOPa on the concantenation of training sections of all seven languages. 
 Question: How many languages have this parser been tried on?

SOLUTION: seven

PROBLEM: To evaluate the performance of our approach, we used a subset of the SNIPS BIBREF12 dataset, which is readily available in RASA nlu format.  We use accuracy of intent and entity recognition as our task and metric.  
 Question: How are their changes evaluated?

SOLUTION:
The changes are evaluated based on accuracy of intent and entity recognition on SNIPS dataset