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.

Q:  We use the $\textsc {BERT}_{\textsc {BASE}}$ ensemble from BIBREF3 as the single-hop QA model. 

A: What off-the-shelf QA model was used to answer sub-questions?
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Q: First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., “If a surface has more friction, then an object will travel slower”), and asked to enter two objects, people, or situations to compare. They then created a question, guided by a large number of examples, and were encouraged to be imaginative and use their own words. Second, the LFs were elicited using a novel technique of reverse-engineering them from a set of follow-up questions, without exposing workers to the underlying formalism. This is possible because of the constrained space of LFs. Referring to LF templates (1) and (2) earlier (Section SECREF13 ), these questions are as follows:

From this information, we can deduce the target LF ( INLINEFORM0 is the complement of INLINEFORM1 , INLINEFORM2 , we arbitrarily set INLINEFORM3 =world1, hence all other variables can be inferred).

A: How do they obtain the logical forms of their questions in their dataset?
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Q: We use two state-of-art neural coreference resolution models described by BIBREF2 and BIBREF1 .

A:
What is the state-of-the-art neural coreference resolution model?
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