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 Q]: 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?
[EX A]: 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. 

[EX Q]: The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples. 
 Question: What sentiment analysis dataset is used?
[EX A]: IMDb dataset of movie reviews

[EX Q]: Our approach consists of structuring input to the transformer network to use and guide the self-attention of the transformers, conditioning it on the entity. Our main mode of encoding the input, the entity-first method, is shown in Figure FIGREF4.  We also have an entity-last variant where the entity is primarily observed just before the classification token to condition the [CLS] token's self-attention accordingly.  As an additional variation, we can either run the transformer once per document with multiple [CLS] tokens (a document-level model as shown in Figure FIGREF4) or specialize the prediction to a single timestep (a sentence-level model). 
 Question: In what way is the input restructured?
[EX A]:
In four entity-centric ways - entity-first, entity-last, document-level and sentence-level