TASK DEFINITION: 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.
PROBLEM: The speech content was obtained from the closed captions (CC) provided by the YouTube ASR system which can be though as subtitles.

SOLUTION: What ASR system do they use?

PROBLEM: Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).

As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas.

SOLUTION: Are the word embeddings evaluated?

PROBLEM: Recently, Wen et al. wensclstm15 proposed an RNN-based approach, which outperformed previous methods on several metrics. However, the generated sentences often did not include all desired attributes.

SOLUTION:
How is some information lost in the RNN-based generation models?