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.

[EX Q]: The previously mentioned datasets are all in English 
 Question: For which languages most of the existing MRC datasets are created?
[EX A]: English

[EX Q]: As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yorùbá languages. The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17). 
 Question: What two architectures are used?
[EX A]: fastText CWE-LP

[EX Q]: The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC).  
 Question: What approaches without reinforcement learning have been tried?
[EX A]:
classification, regression, neural methods