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]: We compare our model with two sets of baselines:

MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. 
 Question: What are the baselines?
[EX A]: MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6

[EX Q]: ACL-ARC is a dataset of citation intents released by BIBREF7 . The dataset is based on a sample of papers from the ACL Anthology Reference Corpus BIBREF15 and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field. 
 Question: What is the size of ACL-ARC datasets?
[EX A]: includes 1,941 citation instances from 186 papers

[EX Q]: In this approach the similarity between two words is not strictly based on their co–occurrence frequencies, but rather on the frequencies of the other words which occur with both of them (i.e., second order co–occurrences). This approach has been shown to be successful in quantifying semantic relatedness BIBREF12 , BIBREF13 . 
 Question: What is a second order co-ocurrence matrix?
[EX A]:
frequencies of the other words which occur with both of them (i.e., second order co–occurrences)