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.

Input: Consider Input: E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process.  The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). The training set was also partitioned in order to obtain the paired and unpaired datasets. 

Output: What non-annotated datasets are considered?


Input: Consider Input: To create training data for unanswerable question generation, we use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions. In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc.

Output: How do they ensure the generated questions are unanswerable?


Input: Consider Input: Filter 1: The string match filter deletes all KB triples where the correct answer (e.g., Apple) is a case-insensitive substring of the subject entity name (e.g., Apple Watch). Filter 2: Of course, entity names can be revealing in ways that are more subtle. As illustrated by our French actor example, a person's name can be a useful prior for guessing their native language and by extension, their nationality, place of birth, etc. Our person name filter uses cloze-style questions to elicit name associations inherent in BERT, and deletes KB triples that correlate with them.


Output: How is it determined that a fact is easy-to-guess?