TASK DEFINITION: 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.
PROBLEM: 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? 

SOLUTION: Yes

PROBLEM: We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English.  
 Question: Which languages are used in the multi-lingual caption model?

SOLUTION: German-English, French-English, and Japanese-English

PROBLEM:   In a second step, we will train a neural monolingual translation system, that translates from the output of the PBMT system INLINEFORM0 to a better target sentence INLINEFORM1 . 
 Question: Do they train the NMT model on PBMT outputs?

SOLUTION:
Yes