TASK DEFINITION: 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.
PROBLEM: he first dataset (WIKI) was the same set of 200 sentences from Wikipedia used in BIBREF7 .  The second dataset (SCI) was a set of 220 sentences from the scientific literature. We sourced the sentences from the OA-STM corpus. In total 2247 triples were extracted. In total, 11262 judgements were obtained after running the annotation process. 
 Question: What is the size of the released dataset?

SOLUTION: 440 sentences, 2247 triples extracted from those sentences, and 11262 judgements on those triples.

PROBLEM: 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?

SOLUTION: avoids the need for expensive cross-validation for hyperparameter selection

PROBLEM: We quantify the efficiency-accuracy tradeoff compared to two rule-based baselines: Unif and Stopword.  
 Question: What are the baselines used?

SOLUTION:
Unif and Stopword