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.

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. 
 Question: Do they explore how much traning data is needed for which magnitude of improvement for WER? 
Yes

The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs. 
 Question: How well did the baseline perform?
accuracy of 30.3% on single sentences and 0.3 on complete paragraphs

From the chart, it is clear that there is a higher chance of fake news coming from unverified accounts. Notice that accounts spreading viral tweets with fake news have, on average, a larger ratio of friends/followers.  Figure FIGREF24 shows that, in contrast to other kinds of viral tweets, those containing fake news were created more recently. 
 Question: What are the characteristics of the accounts that spread fake news?

Accounts that spread fake news are mostly unverified, recently created and have on average high friends/followers ratio