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: We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies BIBREF7 .
Example Output: Do they use datasets with transcribed text or do they determine text from the audio?

Example Input: One-Vs-The-Rest strategy was adopted for the task of multi-class classification and I reported F-score, micro-F, macro-F and weighted-F scores using 10-fold cross-validation.
Example Output: What metrics are considered?

Example Input: In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. In this work, we investigate the application of BERT-based models for the task of subjective language detection. Experiments ::: Dataset and Experimental Settings
We perform our experiments on the WNC dataset open-sourced by the authors of BIBREF2. It consists of aligned pre and post neutralized sentences made by Wikipedia editors under the neutral point of view. It contains $180k$ biased sentences, and their neutral counterparts crawled from $423,823$ Wikipedia revisions between 2004 and 2019
Example Output:
Which experiments are perfomed?