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.

Hausa language is the second most spoken indigenous language in Africa with over 40 million native speakers BIBREF20, and one of the three major languages in Nigeria, along with Igbo and Yorùbá. The language is native to the Northern part of Nigeria and the southern part of Niger, and it is widely spoken in West and Central Africa as a trade language in eight other countries: Benin, Ghana, Cameroon, Togo, Côte d'Ivoire, Chad, Burkina Faso, and Sudan. Yorùbá language is the third most spoken indigenous language in Africa after Swahilli and Hausa with over 35 million native speakers BIBREF20. The language is native to the South-western part of Nigeria and the Southern part of Benin, and it is also spoken in other countries like Republic of Togo, Ghana, Côte d'Ivoire, Sierra Leone, Cuba and Brazil.
In which countries are Hausa and Yor\`ub\'a spoken?

We compare the above model to a similar model, where rather than explicitly represent $K$ features as input, we have $K$ features in the form of a genre embedding, i.e. we learn a genre specific embedding for each of the gothic, scifi, and philosophy genres, as studied in BIBREF8 and BIBREF7.
Is this style generator compared to some baseline?

In all our experiments, we used the out-of-the-box BERT models without any task-specific fine-tuning.

How does their model differ from BERT?