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: 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.
Example Output: Which corpus do they use?

Example Input: To create training data for unanswerable question generation, we use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions. In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc.
Example Output: How do they ensure the generated questions are unanswerable?

Example Input: We label each review vector with the car it reviews. 
Example Output:
What are labels in car speak language dataset?