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.
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Question: 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?

Answer: Use an existing one


Question: We only used models of dimension 300 and, for Word2Vec, Wang2Vec, and FastText, skip-gram architectured models. 
 Question: what word embedding techniques did they experiment with?

Answer: Word2Vec, Wang2Vec, and FastText


Question: Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ).  AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test.  
 Question: By how much does their method outperform state-of-the-art OOD detection?

Answer:
AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average