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: Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks.
Question: What hyperparameters have been tuned?

A: number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks
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Q: We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
Question: How many tweats does MMHS150k contains, 150000?

A: $150,000$ tweets
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Q: Using the LDA model, each person in the dataset is with a topic probability vector $X_i$ . Assume $x_{ik}\in X_{i}$ denotes the likelihood that the $\emph {i}^{th}$ tweet account favors $\emph {k}^{th}$ topic in the dataset. Our topic based features can be calculated as below. Global Outlier Standard Score measures the degree that a user's tweet content is related to a certain topic compared to the other users. Local Outlier Standard Score measures the degree of interest someone shows to a certain topic by considering his own homepage content only. Three baseline classification methods: Support Vector Machines (SVM), Adaboost, and Random Forests are adopted to evaluate our extracted features.
Question: How do they detect spammers?

A:
Extract features from the LDA model and use them in a binary classification task
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