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.

[Q]: We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset. 
 Question: How big is LibriSpeech dataset?
[A]: 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset


[Q]: Four factual attributes pertaining to the 200 colleges are extracted from the U.S. News Statistics, which consists of Undergraduate Enrollment, Male/Female Ratio, Private/Public, and Region (Northeast, South, West, and Midwest).  
 Question: Which major geographical regions are studied?
[A]: Northeast U.S, South U.S., West U.S. and Midwest U.S.


[Q]: This decoding approach closely follows Algorithm SECREF7 , but along with soft back pointers, we also compute hard back pointers at each time step. After computing all the relevant quantities like model score, loss etc., we follow the hard backpointers to obtain the best sequence INLINEFORM0 . 
 Question: Do they compare partially complete sequences (created during steps of beam search) to gold/target sequences?
[A]:
Yes