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]: The adaptive multi-curricula learning framework is established upon the reinforcement learning (RL) paradigm. Figure FIGREF18 illustrates the overall learning process. The multi-curricula learning scheme is scheduled according to the model's performance on the validation set, where the scheduling mechanism acts as the policy $\pi $ interacting with the dialogue model to acquire the learning status $s$. The reward of the multi-curricula learning mechanism $m_t$ indicates how well the current dialogue model performs. A positive reward is expected if a multi-curricula scheduling action $a_t$ brings improvements on the model's performance, and the current mini-batch of training samples is drawn consulting with the scheduling action $a_t$.
[EX A]: How does framework automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model?

[EX Q]: According the distribution of the sentiment score, the sentiment on Tesla is slightly skewed towards positive during the testing period.
[EX A]: Do the authors give any examples of major events which draw the public's attention and the impact they have on stock price?

[EX Q]: Performing the Welch's t-test, both changes after weGAN training are statistically significant at a INLINEFORM0 significance level. 
[EX A]:
Do they evaluate grammaticality of generated text?