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.

[EX Q]: Results show that the CJFA encoder obtains significantly better phone classification accuracy than the VAE baseline and also than the CJFS encoder. These results are replicated for speaker recognition tasks. 
 Question: Which approach out of two proposed in the paper performed better in experiments?
[EX A]: CJFA encoder 

[EX Q]: We would expect that explanation performance should correlate with prediction performance. Since Possible-answer knowledge is primarily needed to decide if the net has enough information to answer the challenge question without guessing and relevant-variable knowledge is needed for the net to know what to query, we analyzed the network's performance on querying and answering separately. The memory network has particular difficulty learning to query relevant variables, reaching only about .5 accuracy when querying. At the same time, it learns to answer very well, reaching over .9 accuracy there. Since these two parts of the interaction are what we ask it to explain in the two modes, we find that the quality of the explanations strongly correlates with the quality of the algorithm executed by the network. 
 Question: How do they measure correlation between the prediction and explanation quality?
[EX A]: They look at the performance accuracy of explanation and the prediction performance

[EX Q]: Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation. 
 Question: Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
[EX A]:
 continuous relaxation to top-k-argmax