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: To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements.
Question: How big is dataset used for fine-tuning BERT?

A: hundreds of thousands of legal agreements
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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.
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Q: We start by crawling the content of a few notable debating websites: idebate.com, debatewise.org, procon.org.
Question: What debate websites did they look at?

A:
idebate.com debatewise.org procon.org
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