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.

Input: Consider Input: Second, scrapping Tweets directly from Twitter website. Using the second option, the daily Tweets for stocks of interest from 2015 January to 2017 June were downloaded. For this reason, two companies from the same industry, Tesla and Ford are investigated on how Twitter sentiment could impact the stock price. To translate each tweet into a sentiment score, the Stanford coreNLP software was used.

Output: Which tweets are used to output the daily sentiment signal?


Input: Consider Input: We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities.

Output: What annotations are available in the dataset - tweat used hate speach or not?


Input: Consider Input: As can be seen, the proposed DSC loss outperforms the best baseline results by a large margin, i.e., outperforming BERT-tagger by +1.86 in terms of F1 score on CTB5, +1.80 on CTB6 and +2.19 on UD1.4.


Output: What are method's improvements of F1 w.r.t. baseline BERT tagger for Chinese POS datasets?