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: Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey. 
 Question: Which sports clubs are the targets?

Output: Galatasaray Fenerbahçe


Input: Consider Input: replacing this single GRU with two different components first component is a sentence reader second component is the input fusion layer 
 Question: What changes they did on input module?

Output: For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader The second component is the input fusion layer


Input: Consider Input: We propose the following challenge to the community: We must develop formal definition and evaluation for faithfulness that allows us the freedom to say when a method is sufficiently faithful to be useful in practice.

We note two possible approaches to this end:

Across models and tasks: The degree (as grayscale) of faithfulness at the level of specific models and tasks. Perhaps some models or tasks allow sufficiently faithful interpretation, even if the same is not true for others.

For example, the method may not be faithful for some question-answering task, but faithful for movie review sentiment, perhaps based on various syntactic and semantic attributes of those tasks.

Across input space: The degree of faithfulness at the level of subspaces of the input space, such as neighborhoods of similar inputs, or singular inputs themselves. If we are able to say with some degree of confidence whether a specific decision's explanation is faithful to the model, even if the interpretation method is not considered universally faithful, it can be used with respect to those specific areas or instances only. 
 Question: What approaches they propose?


Output: Across models and tasks: The degree (as grayscale) of faithfulness at the level of specific models and tasks. Across input space: The degree of faithfulness at the level of subspaces of the input space, such as neighborhoods of similar inputs, or singular inputs themselves.