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:
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:
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). 
 Question: What dataset do they use?

Ex Output:
English$\rightarrow $Italian/German portions of the MuST-C corpus As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)


Ex Input:
We use the word embeddings for Vietnamese that created by Kyubyong Park and Edouard Grave at al:

Kyubyong Park: In his project, he uses two methods including fastText and word2vec to generate word embeddings from wikipedia database backup dumps. Edouard Grave et al BIBREF11: They use fastText tool to generate word embeddings from Wikipedia. 
 Question: What word embeddings were used?

Ex Output:

Kyubyong Park Edouard Grave et al BIBREF11