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.

[EX Q]: GPT-2 achieves the highest score and the $n$-gram the lowest. Transformer-XL and the LSTM LM perform in the middle, and at roughly the same level as each other. We report the 12-category accuracy results for all models and human evaluation in Table TABREF14.
[EX A]: What is the performance of the models on the tasks?

[EX Q]: Our goal was to generate questions without templates and with minimal human involvement using machine learning transformers that have been demonstrated to train faster and better than RNNs. Such a system would benefit educators by saving time to generate quizzes and tests.
[EX A]: What is the motivation behind the work? Why question generation is an important task?

[EX Q]: To learn better graph representation of multi-party dialogues, we adopt the dialogues with 8-15 utterances and 3-7 speakers. To simplify the task, we filter the dialogues with long sentences (more than 20 words). Finally, we obtain 52,053 dialogues and 460,358 utterances.
[EX A]:
How large is the proposed dataset?