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: In Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3 . 
 Question: What was their performance on emotion detection?
Example Output: Answer with content missing: (Table 3) Best author's model B-M average micro f-score is 0.409, 0.459, 0.411 on Affective, Fairy Tales and ISEAR datasets respectively. 

Example Input: Our semi-supervised approach is quite straightforward: first a model is trained on the training set and then this model is used to predict the labels of the silver data. This silver data is then simply added to our training set, after which the model is retrained. However, an extra step is applied to ensure that the silver data is of reasonable quality. 
 Question: What semi-supervised learning is applied?
Example Output: first a model is trained on the training set and then this model is used to predict the labels of the silver data This silver data is then simply added to our training set, after which the model is retrained

Example Input: Ensemble, which selects a sentence using 100 agents trained on clustered dialogues as described in section SECREF4 – the agent in focus is chosen using a regressor as predictor of dialogue reward INLINEFORM0 using a similar neural net as the ChatDQN agents except for the final layer having one node and using Batch Normalisation BIBREF44 between hidden layers as in BIBREF36 ; 
 Question: How many agents do they ensemble over?
Example Output:
100