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]: As shown in Table TABREF12, the T-T model significantly outperforms the LSTM-based RNN-T baseline.
[EX A]: What was previous state of the art model?

[EX Q]: The E2E NLG challenge dataset BIBREF21 is utilized in our experiments, which is a crowd-sourced dataset of 50k instances in the restaurant domain. Our models are trained on the official training set and verified on the official testing set.
[EX A]: What datasets did they use?

[EX Q]: In this work, we propose to generate unanswerable questions by editing an answerable question and conditioning on the corresponding paragraph that contains the answer. So the generated unanswerable questions are more lexically similar and relevant to the context. Moreover, by using the answerable question as a prototype and its answer span as a plausible answer, the generated examples can provide more discriminative training signal to the question answering model.
[EX A]:
Does their approach require a dataset of unanswerable questions mapped to similar answerable questions?