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.

Example Input: From the in-corpus experiments, we obtain good results on SarcasmCorpus, which is the only corpus containing Amazon reviews. Unfortunately, when we train our models in a cross-corpora or all-corpora setting, our results drop dramatically, especially in the cross-corpora case. These results mean that the sarcasm in SarcasmCorpus is conveyed through features that are not present in the other corpora.
Example Output: In which domains is sarcasm conveyed in different ways?

Example Input: Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings.
Example Output: How big is improvement over the old state-of-the-art performance on CoNLL-2009 dataset?

Example Input: . We could observe that compared with the base model, we have an improvement of 7.36% on accuracy and 9.69% on F1 score.
Example Output:
How big are improvements of small-scale unbalanced datasets when sentence representation is enhanced with topic information?