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: EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. 
 Question: what datasets were used?
Example Output: Friends EmotionPush

Example Input: One way to analyze the model is to compute model gradients with respect to input features BIBREF26, BIBREF25. Figure FIGREF37 shows that in this particular example, the most important model inputs are verbs possibly associated with the entity butter, in addition to the entity's mentions themselves. It further shows that the model learns to extract shallow clues of identifying actions exerted upon only the entity being tracked, regardless of other entities, by leveraging verb semantics. 
 Question: What evidence do they present that the model attends to shallow context clues?
Example Output: Using model gradients with respect to input features they presented that the most important model inputs are verbs associated with entities which shows that the model attends to shallow context clues

Example Input: First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21. 
 Question: Which models are used to solve NER for Nepali?
Example Output:
BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 CNN modelBIBREF0 and Stanford CRF modelBIBREF21