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.

Input: Consider Input: 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?

Output: MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6


Input: Consider Input: Named Entity Recognition (NER) in the Biomedical domain usually includes recognition of entities such as proteins, genes, diseases, treatments, drugs, etc.  
 Question: What is NER?

Output: Named Entity Recognition


Input: Consider Input: Excluding the embedding weights, our model requires 100k parameters , while BIBREF8 requires 250k parameters. 
 Question: how many parameters did their model use?


Output: Excluding the embedding weights, our model requires 100k parameters