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: 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?

SOLUTION: Yes

PROBLEM: RKS approach proposed in BIBREF21, BIBREF22, explicitly maps data vectors to a space where linear separation is possible. The RKS method provides an approximate kernel function via explicit mapping. 
 Question: What is the Random Kitchen Sink approach?

SOLUTION: Random Kitchen Sink method uses a kernel function to map data vectors to a space where linear separation is possible.

PROBLEM: Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons. 
 Question: How many question types do they find in the datasets analyzed?

SOLUTION:
seven