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.

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

Shared Self-attention Layers As our model providing two outputs from one input, there is a bifurcation setting for how much shared part should be determined. Both constituent and dependency parsers share token representation and 8 self-attention layers at most. Assuming that either parser always takes input information flow through 8 self-attention layers as shown in Figure FIGREF4, then the number of shared self-attention layers varying from 0 to 8 may reflect the shared degree in the model. When the number is set to 0, it indicates only token representation is shared for both parsers trained for the joint loss through each own 8 self-attention layers. When the number is set to less than 8, for example, 6, then it means that both parsers first shared 6 layers from token representation then have individual 2 self-attention layers. For different numbers of shared layers, the results are in Table TABREF14. We respectively disable the constituent and the dependency parser to obtain a separate learning setting for both parsers in our model. The comparison in Table TABREF14 indicates that even though without any shared self-attention layers, joint training of our model may significantly outperform separate learning mode. At last, the best performance is still obtained from sharing full 8 self-attention layers. 
 Question: How are different network components evaluated?
For different numbers of shared layers, the results are in Table TABREF14. We respectively disable the constituent and the dependency parser to obtain a separate learning setting for both parsers in our model. 

Although humor is a universal construct, there is a wide variety between what each individual may find humorous. We attempt to focus on a subset of the population where we can quantitatively measure reactions: the popular Reddit r/Jokes thread. This forum is highly popular - with tens of thousands of jokes being posted monthly and over 16 million members. Although larger joke datasets exist, the r/Jokes thread is unparalleled in the amount of rated jokes it contains. To the best of our knowledge there is no comparable source of rated jokes in any other language. These Reddit posts consist of the body of the joke, the punchline, and the number of reactions or upvotes. Although this type of humor may only be most enjoyable to a subset of the population, it is an effective way to measure responses to jokes in a large group setting. 
 Question: What kind of humor they have evaluated?

a subset of the population where we can quantitatively measure reactions: the popular Reddit r/Jokes thread These Reddit posts consist of the body of the joke, the punchline, and the number of reactions or upvotes.