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: According the distribution of the sentiment score, the sentiment on Tesla is slightly skewed towards positive during the testing period.

Output: Do the authors give any examples of major events which draw the public's attention and the impact they have on stock price?


Input: Consider Input: We discuss two core models for addressing sequence labeling problems and describe, for each, training them in a single-model multilingual setting: (1) the Meta-LSTM BIBREF0 , an extremely strong baseline for our tasks, and (2) a multilingual BERT-based model BIBREF1 .

Output: What is the multilingual baseline?


Input: Consider Input: Our multi-task BERT models involve six different Arabic classification tasks. Author profiling and deception detection in Arabic (APDA). LAMA+DINA Emotion detection. Sentiment analysis in Arabic tweets.


Output: What are the tasks used in the mulit-task learning setup?