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]: According the distribution of the sentiment score, the sentiment on Tesla is slightly skewed towards positive during the testing period.
[A]: Do the authors give any examples of major events which draw the public's attention and the impact they have on stock price?


[Q]: Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv.
[A]: Do the methods that work best on academic papers also work best on Wikipedia?


[Q]: They used 20-million-words data randomly sampled from the raw text released by the CoNLL 2017 Shared Task - Automatically Annotated Raw Texts and Word Embeddings BIBREF8, which is a combination of Wikipedia dump and common crawl.  For example, we compared the Latvian model by ELMoForManyLangs with a model we trained on a complete (wikidump + common crawl) Latvian corpus, which has about 280 million tokens.
[A]:
How larger are the training sets of these versions of ELMo compared to the previous ones?