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.

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?
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. 

We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message). 
 Question: What baselines did they consider?
state-of-the-art PDTB taggers

Our idea is the BQ generation for MQ and, at the same time, we only want the minimum number of BQ to represent the MQ, so modeling our problem as $LASSO$ optimization problem is an appropriate way 
 Question: What they formulate the question generation as?

LASSO optimization problem