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.
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Question: According the distribution of the sentiment score, the sentiment on Tesla is slightly skewed towards positive during the testing period.

Answer: Do the authors give any examples of major events which draw the public's attention and the impact they have on stock price?

Question: 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.

Answer: Why is lemmatization not necessary in English?

Question: Notably, disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2%. However, effects of this operation vary across tasks, and for QNLI and MNLI, it produces a performance drop of up to -0.2%.

Answer:
How much is performance improved by disabling attention in certain heads?