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.

Given the fact that the research on offensive language detection has to a large extent been focused on the English language, we set out to explore the design of models that can successfully be used for both English and Danish.
What is the challenge for other language except English

Although we create new models for tasks such as sentiment analysis and gender detection as part of AraNet, our focus is more on putting together the toolkit itself and providing strong baselines that can be compared to. Hence, although we provide some baseline models for some of the tasks, we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models) . For many of the tasks we model, there have not been standard benchmarks for comparisons across models. This makes it difficult to measure progress and identify areas worthy of allocating efforts and budgets.
What models did they compare to?

The main reason is that the datasets only contain a small portion of multi-aspect sentences with different polarities. The distraction of attention will not impact the sentiment prediction much in single-aspect sentences or multi-aspect sentences with the same polarities.

Is the model evaluated against the baseline also on single-aspect sentences?