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: Most of the results seem to match with the views of some experts such as the political pundits of the Washington Post.

Answer: Who are the experts?

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: The Flickr30K dataset BIBREF0 is a collection of over 30,000 images with 5 crowdsourced descriptions each. It is commonly used to train and evaluate neural network models that generate image descriptions (e.g. BIBREF2 ). This paper aims to give an overview of linguistic bias and unwarranted inferences resulting from stereotypes and prejudices. I will build on earlier work on linguistic bias in general BIBREF3 , providing examples from the Flickr30K data, and present a taxonomy of unwarranted inferences. Finally, I will discuss several methods to analyze the data in order to detect biases.

Answer:
What is the size of the dataset?