TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: 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.

PROBLEM: As a baseline for the RNN models, we apply a uni-directional RNN which predicts the relation after processing the whole sentence.
Question: Which variant of the recurrent neural network do they use?

SOLUTION: uni-directional RNN

PROBLEM: We evaluate the performance of our proposed method using two criteria: i) rank-correlation BIBREF25 to evaluate visual grounding and ii) accuracy to evaluate question answering. Intuitively, rank-correlation measures the similarity between human and model attention maps under a rank-based metric. A high rank-correlation means that the model is 'looking at' image areas that agree to the visual information used by a human to answer the same question. In terms of accuracy of a predicted answer INLINEFORM0 is evaluated by: DISPLAYFORM0

Question: How do they measure the correlation between manual groundings and model generated ones?

SOLUTION:
rank-correlation BIBREF25