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 sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13. SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences.  
 Question: What baselines did they consider?

A: LSTM SCIBERT
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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?

A: They look at the performance accuracy of explanation and the prediction performance
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Q: Our study uses WikiHop BIBREF0, as it is an entity-based multi-hop QA dataset and has been actively used. 
 Question: What dataset was used in the experiment?

A:
WikiHop
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