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.

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?
hundreds of thousands of legal agreements

For annotation purposes, we created snippets of conversations as the ones shown in Example 1 and Example 2 consisting of the parent of the suspected trolling event, the suspected trolling event comment, and all of the direct responses to the suspected trolling event. However, the trade off is that snippets like this allow us to make use of Amazon Mechanical Turk (AMT) to have the dataset annotated, because it is not a big burden for a “turker” to work on an individual snippet in exchange for a small pay, and expedites the annotation process by distributing it over dozens of people. Specifically, for each snippet, we requested three annotators to label the four aspects previously described. 
 Question: how was annotation done?
Annotation was done with the help of annotators from Amazon Mechanical Turk on snippets of conversations

For both datasets, we follow the evaluation metrics used in the original evaluation tasks BIBREF13 . For DBQA, P@1 (Precision@1), MAP (Mean Average Precision) and MRR (Mean Reciprocal Rank) are adopted. For KBRE, since only one golden candidate is labeled for each question, only P@1 and MRR are used. 
 Question: Which metrics do they use to evaluate matching?

Precision@1 Mean Average Precision Mean Reciprocal Rank