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.

Because our approach is specifically intended to yield sentences that are grammatical, we additionally consider the following log ratio (i.e., the grammatical phrase over the ungrammatical phrase):
How do they measure grammaticality?

We present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that "understands" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs.
What knowledge bases do they use?

A selection of results from this study are that:

Non-English code is a large-scale phenomena.

Transliteration is common in identifiers for all languages.

Languages clusters into three distinct groups based on how speakers use identifiers/comments/transliteration.

Non-latin script users write comments in their L1 script but write identifiers in English.

Right-to-left (RTL) language scripts, such as Arabic, have no observed prevalence on GitHub identifiers, implying that existing coders who speak RTL languages have substantial barriers in using their native script in code.
What are results of public code repository study?