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]: We acquired the data in two rounds of annotation. In the first one, we were looking for original and uncommon sentence change suggestions.
[A]: How do they introduce language variation?


[Q]: The BioNLP 2009 Shared Task BIBREF195 was based on the GENIA corpus BIBREF196 which contains PubMed abstracts of articles on transcription factors in human blood cells.
[A]: Which datasets are used in this work?


[Q]: What is the impact of pre-trained representations with less transcribed data? In order to get a better understanding of this, we train acoustic models with different amounts of labeled training data and measure accuracy with and without pre-trained representations (log-mel filterbanks). The pre-trained representations are trained on the full Librispeech corpus and we measure accuracy in terms of WER when decoding with a 4-gram language model. Figure shows that pre-training reduces WER by 32% on nov93dev when only about eight hours of transcribed data is available. Pre-training only on the audio data of WSJ ( WSJ) performs worse compared to the much larger Librispeech ( Libri). This further confirms that pre-training on more data is crucial to good performance.
[A]:
Do they explore how much traning data is needed for which magnitude of improvement for WER?