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.

We use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter selection
Question: Why is a Gaussian process an especially appropriate method for this classification problem?
avoids the need for expensive cross-validation for hyperparameter selection

To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs.
Question: What was the baseline model?
a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs

The main set of experiments uses a Linear Support Vector Machine (Joachims, 1998) to classify dialects using CxG features. This paper has used data-driven language mapping to select national dialects of English to be included in a global dialect identification model.
Question: What do the models that they compare predict?
national dialects of English