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.

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

Example Input: We collected the dataset with 1900 dialogs and 8533 turns. 
Example Output: What is the average length of dialog?

Example Input: We use 300 Dimensional Glove Common Crawl Embeddings (840B Token) BIBREF11 and fine tune them for the task
Example Output:
What embedding algorithm and dimension size are used?