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: To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs.  
 Question: What was the baseline model?

Output: a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs


Input: Consider Input: The second algorithm incorporates recent ideas of data selection in machine translation BIBREF7 . We develop a novel re-weighting method to resolve these problems, using ideas inspired by data selection in machine translation BIBREF26 , BIBREF7 . 
 Question: What is the data selection paper in machine translation

Output: BIBREF7 BIBREF26 


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