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.

Ex Input:
Even if LangID-High does not present a more accurate result, it does present a more compact one: LangID-High is 15.4 MB, while the combined wFST high resource models are 197.5 MB.

Ex Output:
how is model compactness measured?

Ex Input:
Encoders with induced latent structures have been shown to benefit several tasks including document classification, natural language inference BIBREF12, BIBREF13, and machine translation BIBREF11.

Ex Output:
Is there any evidence that encoders with latent structures work well on other tasks?

Ex 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.

Ex Output:
Is this a span-based (extractive) QA task?