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.

Example Input: 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? 
Example Output: Yes

Example Input: 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?
Example Output: Word2Vec, Wang2Vec, and FastText

Example Input: We perform a direct comparison of expert and crowd contributors, for 1000 term groups based on the number of total annotations(200 term groups with 2 total annotations, 200 term groups with 3 total annotations, and so on up to term groups with 6 total annotations).  Evaluators were given a summary of the annotations received for the term group in the form of:The term group "inequality inequity" received annotations as 50.0% sadness, 33.33% disgust, 16.67% anger. Then, they were asked to evaluate on a scale from 1 to 5, how valid these annotations were considered. 
 Question: How do they compare lexicons?
Example Output:
Human evaluators were asked to evaluate on a scale from 1 to 5 the validity of the lexicon annotations made by the experts and crowd contributors.