TASK DEFINITION: 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.
PROBLEM: 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.

SOLUTION: How did the authors demonstrate that showing a hate speech definition caused annotators to partially align their own opinion with the definition?

PROBLEM: For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains The evaluation of the German version is in progress.

SOLUTION: What are the corpora used for the task?

PROBLEM: However, what we did was more than this, and we also achieved great results on the second-level and third-level senses for the sake of our self-demand for high-quality, finally achieving agreement of 0.85 and Kappa value of 0.83 for these two deeper levels of senses.

SOLUTION:
How high is the inter-annotator agreement?