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: Out of 43 teams our system ranked 421st with an official F1-score of 0.2905 on the test set. Although our model outperforms the baseline in the validation set in terms of F1-score, we observe important drops for all metrics compared to the test set, showing that the architecture seems to be unable to generalize well.
Example Output: What were their results on the test set?

Example Input: We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29.
Example Output: Is this a span-based (extractive) QA task?

Example Input: Dataset: Based on some earlier work, only available labelled dataset had 3189 rows of text messages of average length of 116 words and with a range of 1, 1295.
Example Output:
How big is the dataset?