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.

[EX Q]: Participants who were shown the definition were more likely to suggest to ban the tweet. In fact, participants in group one very rarely gave different answers to questions one and two (18 of 500 instances or 3.6%). This suggests that participants in that group aligned their own opinion with the definition.
[EX A]: How did the authors demonstrate that showing a hate speech definition caused annotators to partially align their own opinion with the definition?

[EX Q]: Compared with the BERT-BASE model, BERT-SPC significantly improves the accuracy and F1 score of aspect polarity classification.
[EX A]: How much better is performance of the proposed model compared to the state of the art in these various experiments?

[EX Q]: These datasets are part of SemEval-2016 Challenge Task 5 BIBREF27 , BIBREF28 . Table TABREF7 shows the number of observations in each test corpus.
[EX A]:
what datasets were used in evaluation?