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.

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).
How do they obtain the logical forms of their questions in their dataset?

Comparing the natural and artificial sources of our parallel data wrt. several linguistic and distributional properties, we observe that (see Fig. FIGREF21 - FIGREF22 ):

artificial sources are on average shorter than natural ones: when using BT, cases where the source is shorter than the target are rarer; cases when they have the same length are more frequent.

automatic word alignments between artificial sources tend to be more monotonic than when using natural sources, as measured by the average Kendall INLINEFORM0 of source-target alignments BIBREF22 : for French-English the respective numbers are 0.048 (natural) and 0.018 (artificial); for German-English 0.068 and 0.053.  The intuition is that properties (i) and (ii) should help translation as compared to natural source, while property (iv) should be detrimental.
what is their explanation for the effectiveness of back-translation?

Among all three pre-training tasks, SR works slightly better than the other two tasks (i.e., NSG and MDG).

Which of the three pretraining tasks is the most helpful?