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.

Input: Consider Input: We empirically test our approach on a series of experiments on WikiTableQuestions, to our knowledge the only dataset designed for this task. In Algorithm 1 we describe how logical forms are transformed into interpretable textual representations called "paraphrases". 
 Question: What is the source of the paraphrases of the questions?

Output: WikiTableQuestions


Input: Consider Input: As a framework, we use the sentence classifier configuration of FLAIR BIBREF46 with a biLSTM encoder/classifier architecture fed by character and word level representations composed of a concatenation of fixed 300 dimensional GloVe embeddings BIBREF47, pre-trained contextualized FLAIR word embeddings, and pre-trained contextualized character embeddings from AllenNLP BIBREF48 with FLAIR's default hyperparameters. 
 Question: Are some models evaluated using this metric, what are the findings?

Output: Yes


Input: Consider Input: To build the classifiers we used three different learning algorithms, namely Logistic Regression (LR), Random Forest (RF), and Support Vector Machines (SVM). 
 Question: Which supervised learning algorithms are used in the experiments?


Output: Logistic Regression (LR) Random Forest (RF) Support Vector Machines (SVM)