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 Input:
 For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English .  
 Question: What monolingual word representations are used?

Ex Output:
AraVec for Arabic, FastText for French, and Word2vec Google News for English.


Ex Input:
In this paper we have formalized the problem of automatic fill-on-the-blanks quiz generation using two well-defined learning schemes: sequence classification and sequence labeling. 
 Question: Which two schemes are used?

Ex Output:
sequence classification sequence labeling


Ex Input:
As shown in Figure FIGREF7 , KAR is an end-to-end MRC model consisting of five layers:

Lexicon Embedding Layer. This layer maps the words to the lexicon embeddings. Context Embedding Layer. This layer maps the lexicon embeddings to the context embeddings. Coarse Memory Layer. This layer maps the context embeddings to the coarse memories. Refined Memory Layer. This layer maps the coarse memories to the refined memories. Answer Span Prediction Layer. This layer predicts the answer start position and the answer end position based on the above layers. 
 Question: What type of model is KAR?

Ex Output:

Lexicon Embedding Layer Context Embedding Layer Coarse Memory Layer Refined Memory Layer Answer Span Prediction Layer