In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: The baseline classifier uses a linear Support Vector Machine BIBREF7 , which is suited for a high number of features. 

A: What baseline is used?
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Q: The retrieved pairs are compared to the gold standard and evaluated using precision at $k$ (P@$k$, evaluating how often the correct translation is within the $k$ retrieved nearest neighbours of the query). Throughout this work we report P@1, which is equivalent to accuracy, but we also provide results with P@5 and P@10 in the Appendix.

A: What evaluation metrics did they use?
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Q: The used classifiers are Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB).

A:
What classical machine learning algorithms are used?
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