Mask-filling: Given a sequence of tables T and a question Q with [MASK], what is the word at [MASK].
Q： Looking at Table 5, we observe a performance drop in both empathy and relevance, by [MASK] and 0.02, respectively, in the absence of emotion grouping. This indicates the importance of having positive and negative emotions treated
T:<C>Model Human Ratings  Emp ., BLEU,Metric Empathy,0.01, Relevance,<max>0.02, Fluency,0.01,<C>Model Emotion  Grouping, BLEU,Metric Empathy,2.45, Relevance,2.96, Fluency,<max>2.98,<C>Model Human Ratings  Rel ., BLEU,Metric Empathy,3.58, Relevance,<max>3.63<bold>, Fluency,3.60,<C>Model MoEL  Pos, BLEU,Metric Empathy,3.73, Relevance,3.21, Fluency,<max>4.35<bold>,<C>Model Human Ratings  Emp ., BLEU,Metric Empathy,3.14, Relevance,3.67, Fluency,<max>3.87<bold>,<C> - TR  Pos, BLEU,Metric Empathy,3.77, Relevance,3.51, Fluency,<max>4.33,<C>Model Human Ratings  Flu ., BLEU,Metric Empathy,4.23, Relevance,4.09, Fluency,<max>4.28<bold>,<C>Model MIME  Pos, BLEU,Metric Empathy,4.00<bold>, Relevance,3.77<bold>, Fluency,<max>4.33,<C> - TR  Neg, BLEU,Metric Empathy,3.61, Relevance,3.45, Fluency,<max>4.28,<C>Model MoEL  Neg, BLEU,Metric Empathy,3.76, Relevance,3.40, Fluency,<max>4.30<bold>,<C>Model MIME  Neg, BLEU,Metric Empathy,3.80<bold>, Relevance,3.49<bold>, Fluency,<max>4.26,Table 4: Models performance on positive and negative context; Pos and Neg stand for positive and negative emotions, respectively; the best score for each metric-polarity combination is highlighted by bold font. 