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.

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

We have shown that transformer-based encoders together with GRU models provide the best performance for question representation. Notably, we demonstrated that using pre-trained text representations provide consistent performance improvements across several hyper-parameter configurations. We have also shown that using an object detector fine-tuned with external data provides large improvements in accuracy. Moreover, we have shown that attention mechanisms are paramount for learning top performing networks, once they allow producing question-aware image representations that are capable of encoding spatial relations. It became clear that Top-Down is the preferred attention method, given its results with ReLU activation. 
 Question: What components are identified as core components for training VQA models?
pre-trained text representations transformer-based encoders together with GRU models attention mechanisms are paramount for learning top performing networks Top-Down is the preferred attention method

We carried out two human evaluations using Mechanical Turk to compare the performance of our model and the baseline. 
 Question: Is there any human evaluation involved in evaluating this famework?

Yes

