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.

Input: Consider Input: Dataset Quality Analysis ::: Inter-Annotator Agreement (IAA)
To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate. Dataset Quality Analysis ::: Dataset Assessment and Comparison
We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates.  Dataset Quality Analysis ::: Agreement with PropBank Data
It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7. 

Output: How was quality measured?


Input: Consider Input: Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37.

Output: What recommendations are made to improve the performance in future?


Input: Consider Input: The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 .


Output: Do the authors test their annotation projection techniques on tasks other than AMR?