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: Following lebret2016neural, we used BLEU-4, NIST-4 and ROUGE-4 as the evaluation metrics. 
 Question: What metrics are used for evaluation?

SOLUTION: BLEU-4 NIST-4 ROUGE-4

PROBLEM: If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model. 
 Question: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?

SOLUTION: INLINEFORM2 

PROBLEM: For the proposed model, we denote INLINEFORM0 parameterized by INLINEFORM1 as a neural-based feature encoder that maps documents from both domains to a shared feature space, and INLINEFORM2 parameterized by INLINEFORM3 as a fully connected layer with softmax activation serving as the sentiment classifier. We have left the feature encoder INLINEFORM0 unspecified, for which, a few options can be considered. In our implementation, we adopt a one-layer CNN structure from previous works BIBREF22 , BIBREF4 , as it has been demonstrated to work well for sentiment classification tasks. 
 Question: What is the architecture of the model?

SOLUTION:
one-layer CNN structure from previous works BIBREF22 , BIBREF4