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 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?

Ex Output:
MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6


Ex Input:
Next, for emergent language, we show that the MASC architecture can achieve very high localization accuracy, significantly outperforming the baseline that does not include this mechanism. 
 Question: What evaluation metrics did the authors look at?

Ex Output:
localization accuracy


Ex Input:
As shown in Table TABREF48, our two models show competitive results compared to the state-of-the-art on the Visual Dialog challenge 2018 (DL-61 was the winner of the Visual Dialog challenge 2018).  
 Question: What model was winner of the Visual Dialog challenge 2018?

Ex Output:

DL-61