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.
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Question: For MSA, we acquired the diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31. The corpus contains 9.7M tokens with approximately 194K unique surface forms (excluding numbers and punctuation marks). For testing, we used the freely available WikiNews test set BIBREF31, which is composed of 70 MSA WikiNews articles (18,300 tokens) and evenly covers a variety of genres including politics, economics, health, science and technology, sports, arts and culture. For CA, we obtained a large collection of fully diacritized classical texts (2.7M tokens) from a book publisher, and we held-out a small subset of 5,000 sentences (approximately 400k words) for testing. 
 Question: what datasets were used?

Answer: diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31 WikiNews test set BIBREF31  large collection of fully diacritized classical texts (2.7M tokens) from a book publisher


Question: 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?

Answer: hundreds of thousands of legal agreements


Question: Specifically, we group the models based on the objective function it optimizes. We believe this work can aid the understanding of the existing literature.  We believe that the performance of these models is highly dependent on the objective function it optimizes – predicting adjacent word (within-tweet relationships), adjacent tweet (inter-tweet relationships), the tweet itself (autoencoder), modeling from structured resources like paraphrase databases and weak supervision.  
 Question: How do they encourage understanding of literature as part of their objective function?

Answer:
They group the existing works in terms of the objective function they optimize - within-tweet relationships, inter-tweet relationships, autoencoder, and weak supervision.