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: The current state-of-the-art approach BIBREF14 , BIBREF15 uses maximum entropy and CRF models with a combination of language model and hand-crafted features to predict if each character in the hashtag is the beginning of a new word.

Output: What current state of the art method was used for comparison?

Input: Consider Input: 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.

Output: What is the performance of the models on the tasks?

Input: Consider Input: BIBREF5 adopt a machine learning approach for NER. Their NER system extracts medical problems, tests and treatments from discharge summaries and progress notes. The dataset used is the i2b2 2010 challenge dataset.
Output: Does the paper explore extraction from electronic health records?