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: replacing this single GRU with two different components first component is a sentence reader second component is the input fusion layer 
 Question: What changes they did on input module?

SOLUTION: For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader The second component is the input fusion layer

PROBLEM: To tackle this issue, we investigate how to improve the generalization of BERT-based MCQA models with the constraint of limited training data using four representative MCQA datasets: DREAM, MCTest, TOEFL, and SemEval-2018 Task 11. 
 Question: What four representative datasets are used for bechmark?

SOLUTION:
DREAM, MCTest, TOEFL, and SemEval-2018 Task 11