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:
The overall Total Accuracy score reported in table TABREF19 using the entire feature set is 549. 

Ex Output:
Do they experiment with the dataset?


Ex Input:
We present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that "understands" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs.

Ex Output:
What knowledge bases do they use?


Ex Input:
We see that parent quality is a simple yet effective feature and SVM model with this feature can achieve significantly higher ($p<0.001$) F1 score ($46.61\%$) than distance from the thesis and linguistic features. Although the BiLSTM model with attention and FastText baselines performs better than the SVM with distance from the thesis and linguistic features, it has similar performance to the parent quality baseline. We find that the flat representation of the context achieves the highest F1 score. It may be more difficult for the models with a larger number of parameters to perform better than the flat representation since the dataset is small. We also observe that modeling 3 claims on the argument path before the target claim achieves the best F1 score ($55.98\%$).

Ex Output:

How better are results compared to baseline models?