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.

Example Input: Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing.
Example Output: What is the architecture of their model?

Example Input: The system consists of: (i) Local, shallow features based mostly on orthographic, word shape and n-gram features plus their context; and (ii) three types of simple clustering features, based on unigram matching: (i) Brown BIBREF32 clusters, taking the 4th, 8th, 12th and 20th node in the path; (ii) Clark BIBREF33 clusters and, (iii) Word2vec BIBREF34 clusters, based on K-means applied over the extracted word vectors using the skip-gram algorithm.
Example Output: What shallow local features are extracted?

Example Input: WikiTableQuestions contains 22,033 questions and is an order of magnitude larger than previous state-of-the-art datasets. Its questions were not designed by predefined templates but were hand crafted by users, demonstrating high linguistic variance.
Example Output:
How do they gather data for the query explanation problem?