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.

Ex Input:
We include ten subreddits in the five domains of abuse, social, anxiety, PTSD, and financial, as detailed in tab:data-spread, and our analysis focuses on the domain level. 

Ex Output:
What categories does the dataset come from?


Ex 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 .

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


Ex Input:
 The first, referred to here as seq2seq, is the standard RNN-based neural machine translation system with attention BIBREF0 . This baseline does not use the parsed data. The multi-source systems improve strongly over both baselines, with improvements of up to 1.5 BLEU over the seq2seq baseline and up to 1.1 BLEU over the parse2seq baseline.

Ex Output:

Whas is the performance drop of their model when there is no parsed input?