TASK DEFINITION: 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.
PROBLEM: 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).

SOLUTION: How do they obtain the logical forms of their questions in their dataset?

PROBLEM: We find that while optimizing its reformulations to adapt to the language of the QA system, AQA diverges from well structured language in favour of less fluent, but more effective, classic information retrieval (IR) query operations. 

SOLUTION: What is the difference in findings of Buck et al? It looks like the same conclusion was mentioned in Buck et al..

PROBLEM: We evaluate the proposed transfer learning techniques in two non-English language pairs of WMT 2019 news translation tasks: French$\rightarrow $German and German$\rightarrow $Czech.

SOLUTION:
Are experiments performed with any other pair of languages, how did proposed method perform compared to other models?