In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

The Software Ontology (SWO) BIBREF5 is included because its set of CQs is of substantial size and it was part of Ren et al.'s set of analysed CQs. The CQ sets of Dem@Care BIBREF8 and OntoDT BIBREF9 were included because they were available. CQs for the Stuff BIBREF6 and African Wildlife (AWO) BIBREF7 ontologies were added to the set, because the ontologies were developed by one of the authors (therewith facilitating in-depth domain analysis, if needed), they cover other topics, and are of a different `type' (a tutorial ontology (AWO) and a core ontology (Stuff)), thus contributing to maximising diversity in source selection. 
 Question: How many domains of ontologies do they gather data from?
Abstractive

We thus adapt an off-the-shelf reward learning algorithm BIBREF7 to the supervised setting for automated data manipulation. 
 Question: What off-the-shelf reward learning algorithm from RL for joint data manipulation learning and model training is adapted?
Extractive

We describe our rules for WikiSQL here. Our rule for KBQA is simple without using a curated mapping dictionary. The pipeline of rules in SequentialQA is similar to that of WikiSQL. 
 Question: Are the rules dataset specific?

Yes-no