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.

Our semi-supervised approach is quite straightforward: first a model is trained on the training set and then this model is used to predict the labels of the silver data. This silver data is then simply added to our training set, after which the model is retrained. However, an extra step is applied to ensure that the silver data is of reasonable quality. 
 Question: What semi-supervised learning is applied?
first a model is trained on the training set and then this model is used to predict the labels of the silver data This silver data is then simply added to our training set, after which the model is retrained

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?
DNN-based acoustic model BIBREF0

With this challenge in mind, we introduce Torch-Struct with three specific contributions:

Modularity: models are represented as distributions with a standard flexible API integrated into a deep learning framework.

Completeness: a broad array of classical algorithms are implemented and new models can easily be added in Python.

Efficiency: implementations target computational/memory efficiency for GPUs and the backend includes extensions for optimization. 
 Question: Is this library implemented into Torch or is framework agnostic?

It uses deep learning framework (pytorch)

