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: To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively.
Question: What was their system's performance?

A: accuracy and F1-score of 89.6% and 89.2%, respectively
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Q: We adopt a DNN-based acoustic model BIBREF0 with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion.
Question: What are the deep learning architectures used in the task?

A: DNN-based acoustic model BIBREF0
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Q: Then, it selects a subset of a 1,700-hour ( INLINEFORM2 1.1M instances) unlabeled dataset.
Question: How much data samples do they start with before obtaining the initial model labels?

A:
1,700-hour ( INLINEFORM2 1.1M instances) unlabeled dataset
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