TASK DEFINITION: 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.
PROBLEM: We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences.  
 Question: How do they perform data augmentation?

SOLUTION: They randomly sample sentences from Wikipedia that contains an object RC and add them to training data

PROBLEM: In case of polysemous words, only the first word sense (usually the most common) is taken into account. 
 Question: How do they handle polysemous words in their entity library?

SOLUTION: only the first word sense (usually the most common) is taken into account

PROBLEM: ACL-ARC is a dataset of citation intents released by BIBREF7 . The dataset is based on a sample of papers from the ACL Anthology Reference Corpus BIBREF15 and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field. 
 Question: What is the size of ACL-ARC datasets?

SOLUTION:
includes 1,941 citation instances from 186 papers