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 Input:
We evaluate our model on two public datasets, namely, Penn Treebank (PTB) BIBREF9 and the end-to-end (E2E) text generation corpus BIBREF10, which have been used in a number of previous works for text generation BIBREF0, BIBREF5, BIBREF11, BIBREF12. PTB consists of more than 40,000 sentences from Wall Street Journal articles whereas the E2E dataset contains over 50,000 sentences of restaurant reviews. The statistics of these two datasets are summarised in Table TABREF11.

Ex Output:
Which dataset do they use for text modelling?

Ex Input:
Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance. We provide human evaluation on our framework and the compared models. We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5.

Ex Output:
What were the evaluation metrics?

Ex Input:
In this section, we empirically compare automatic differentiation (AD, our implementation based on Clad) and numerical differentiation (ND, based on finite difference method) in ROOT. We show that AD can drastically improve accuracy and performance of derivative evaluation, compared to ND.

Ex Output:
How is correctness of automatic derivation proved?