TASK DEFINITION: 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.
PROBLEM: To create our training dataset, we followed an approach similar to LASER. The dataset contains 6 languages: English, Spanish, German, Dutch, Korean and Chinese Mandarin. The dataset was created by using translations provided by Tatoeba and OpenSubtitles BIBREF16.

SOLUTION: Which corpus do they use?

PROBLEM: In this work, we analyze a set of tweets related to a specific classical music radio channel, BBC Radio 3, interested in detecting two types of musical named entities, Contributor and Musical Work.

SOLUTION: What language is the Twitter content in?

PROBLEM: Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin.

SOLUTION:
Is all text in this dataset a question, or are there unrelated sentences in between questions?