In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively 
 Question: How many samples did they generate for the artificial language?
70,000

We use UltraSuite: a repository of ultrasound and acoustic data gathered from child speech therapy sessions BIBREF15 . We used all three datasets from the repository: UXTD (recorded with typically developing children), and UXSSD and UPX (recorded with children with speech sound disorders). In total, the dataset contains 13,815 spoken utterances from 86 speakers, corresponding to 35.9 hours of recordings. The utterances have been categorised by the type of task the child was given, and are labelled as: Words (A), Non-words (B), Sentence (C), Articulatory (D), Non-speech (E), or Conversations (F).  
 Question: Do they annotate their own dataset or use an existing one?
Use an existing one

As a further application of our work, we have carried out a supervised classification task aimed at predicting the degree of harm of an incident directly from the text and the hand-coded features (e.g., external category, medical specialty, location).  We also checked if using our unsupervised content-driven cluster labels as additional features can improve the performance of the supervised classification. 
 Question: How are content clusters used to improve the prediction of incident severity?

they are used as additional features in a supervised classification task