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.

Q: SCA BIBREF5 softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words, i.e., replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary.

A: How does soft contextual data augmentation work?
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Q: We focus primarily on the smaller data set of 645 hand-labeled articles provided to task participants, both for training and for validation. We pre-trained BERT-base on the 600,000 articles without labels by using the same Cloze task BIBREF5 that BERT had originally used for pre-training.

A: How long is the dataset?
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Q: We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies BIBREF7 .

A:
Do they use datasets with transcribed text or do they determine text from the audio?
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