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: 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: Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work.
Question: Do the authors mention any confounds to their study?

A: No
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Q: Secondly, a well-known problem of uneven yet unknown distribution of word senses is alleviated by modifying a naive Bayesian classifier. Thanks to this correction, the classifier is no longer biased towards senses that have more training data. Finally, the aggregated feature values corresponding to target word senses are used to build a naive Bayesian classifier adjusted to a situation of unknown a priori probabilities.
Question: How do they deal with unknown distribution senses?

A:
The Naive-Bayes classifier is corrected so it is not biased to most frequent classes
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