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.

[EX Q]: We used the Visual storytelling (VIST) dataset comprising of image sequences obtained from Flickr albums and respective annotated descriptions collected through Amazon Mechanical Turk BIBREF1. Each sequence has 5 images with corresponding descriptions that together make up for a story. Furthermore, for each Flickr album there are 5 permutations of a selected set of its images. In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories.
[EX A]: What statistics on the VIST dataset are reported?

[EX Q]: Specifically, Roget's Thesaurus BIBREF38 , BIBREF39 is used to derive the concepts and concept word-groups to be used as the external lexical resource for our proposed method. By using the concept word-groups, we have trained the GloVe algorithm with the proposed modification given in Section SECREF4 on a snapshot of English Wikipedia measuring 8GB in size, with the stop-words filtered out.
[EX A]: Do they report results only on English data?

[EX Q]: We compare eight different models in our experiments. Four of them have a Projected Layer (see Fig. FIGREF2), while the others do not have, and this is the only difference between these two groups of our models. So, we actually include four models in our experiments (having a projected layer or not). Firstly, LastStateRNN is the classic RNN model, where the last state passes through an MLP and then the LR Layer estimates the corresponding probability. In contrast, in the AvgRNN model we consider the average vector of all states that come out of the cells. The AttentionRNN model is the one that it has been presented in BIBREF9. Moreover, we introduce the MultiAttentionRNN model for the harassment language detection, which instead of one attention, it includes four attentions, one for each category.
[EX A]:
What are the different variations of the attention-based approach which are examined?