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 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: For all tasks, we use a TensorFlow implementation. AraNet predicts age, dialect, gender, emotion, irony, and sentiment from social media posts. It delivers state-of-the-art and competitive performance on these tasks and has the advantage of using a unified, simple framework based on the recently-developed BERT model.
Question: Did they experiment on all the tasks?

A: Yes
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Q: Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).
Question: Which machine learning models are used?

A:
RNNs CNNs Naive Bayes with Laplace Smoothing k-clustering SVM with linear kernel
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