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.

Q: Advanced neural architectures based on character input (CNNs, BPE, etc) are supposed to be able to learn how to handle spelling and morphology variations themselves, even for languages with rich morphology: `just add more layers!'. Contextualised embedding models follow this tradition: as a rule, they are trained on raw text collections, with minimal linguistic pre-processing. Below, we show that this is not entirely true.

A: Why is lemmatization not necessary in English?
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Q: 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.

A: Is this style generator compared to some baseline?
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Q: We focus on three main issues, including, how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance.

A:
What are the research questions posed in the paper regarding EAC studies?
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