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: We focus on social media posts from the website Twitter, which are an excellent testing ground for character based models due to the noisy nature of text. Heavy use of slang and abundant misspellings means that there are many orthographically and semantically similar tokens, and special characters such as emojis are also immensely popular and carry useful semantic information. In our moderately sized training dataset of 2 million tweets, there were about 0.92 million unique word types.

Output: Does the paper clearly establish that the challenges listed here exist in this dataset and task?


Input: Consider Input: We crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. 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.

Output: Do all questions in the dataset allow the answers to pick from 2 options?


Input: Consider Input: This study focuses on Switchboard-300, a standard 300-hour English conversational speech recognition task. As a contrast to our best results on Switchboard-300, we also train a seq2seq model on the 2000-hour Switchboard+Fisher data. 


Output: How much bigger is Switchboard-2000 than Switchboard-300 database?