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.

[Q]:  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?
[A]: AraVec for Arabic, FastText for French, and Word2vec Google News for English.


[Q]: To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning “diabetes", “cancer", and “influenza" into a topic that has an overall “disease" theme BIBREF44 , BIBREF45 . Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets. 
 Question: How were topics of interest about DDEO identified?
[A]: using topic modeling model Latent Dirichlet Allocation (LDA)


[Q]: The data used for this task is drawn from the Switchboard conversational English corpus BIBREF31 . 
 Question: Which dataset do they use?
[A]:
Switchboard conversational English corpus


