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.

Example Input: As in the Sequicity framework, we report entity match rate, BLEU score and Success F1 score.  
 Question: What were the evaluation metrics used?
Example Output: entity match rate BLEU score Success F1 score

Example Input: Our model contains five independent decoders, one for each image in the sequence. This allows each decoder to learn a specific language model for each position of the sequence. 
 Question: Do the decoder LSTMs all have the same weights?
Example Output: No

Example Input:  First, we analyze the types of structures in code-mixed puns and classify them into two categories namely intra-sequential and intra-word.  
 Question: What are the categories of code-mixed puns?
Example Output:
intra-sequential and intra-word