In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

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


[Q]: We consider two different models for each language pair: the Baseline and the Document model. We evaluate them on 3 test sets and report BLEU and TER scores. All experiments are run 8 times with different seeds, we report averaged results and p-values for each experiment.
[A]: What evaluation metrics did they use?


[Q]: The straight line in figures FIGREF39, FIGREF43 and FIGREF51 is the result of a supervised LDA algorithm which is used as a baseline. 
[A]:
Was performance of the weakly-supervised model compared to the performance of a supervised model?


