In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: We collected the dataset with 1900 dialogs and 8533 turns. 
[A]: What is the average length of dialog?


[Q]: Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37.
[A]: What recommendations are made to improve the performance in future?


[Q]: In other words, there exist a few directions in the embedding space which disproportionately explain the variance. It is clear that the visual features in the case of How2 dataset are much more dominated by the "common" dimensions, when compared to the Multi30k dataset. Further, this analysis is still at the sentence level, i.e. the visual features are much less discriminative among individual sentences, further aggravating the problem at the token level. This suggests that the existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT, since they won't provide discriminativeness among the vocabulary elements at the token level during prediction.
[A]:
What is result of their Principal Component Analysis?