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.

[Q]: 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?
[A]: Yes

[Q]: Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback.
Question: How are workers trained?
[A]: extensive personal feedback

[Q]: TOEFL listening comprehension test is for human English learners whose native language is not English. This paper reports how today's machine can perform with such a test.

The listening comprehension task considered here is highly related to Spoken Question Answering (SQA) BIBREF0 , BIBREF1 .
Question: What is the new task proposed in this work?
[A]:
listening comprehension task