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.

In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am.
what is the source of the news sentences?

Validated transcripts were sent to professional translators. In order to control the quality of the professional translations, we applied various sanity checks to the translations BIBREF11. We also sanity check the overlaps of train, development and test sets in terms of transcripts and voice clips (via MD5 file hashing), and confirm they are totally disjoint.
How is the quality of the data empirically evaluated?

More specifically, in our model we use the context of the word to predict its label and by doing so our model learns label-aware context for each word in the sentence. In order to improve the interactivity between the word representation and its context, we increase the mutual information between the word representations and its context.
How does their model utilize contextual information for each work in the given sentence in a multi-task setting? setting?