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: S2-GORC is a large citation graph dataset which includes full texts of 8.1 million scientific documents. We select a subset of 154K computer science articles as our corpus. From these, we extract 622K citing sentences that link back to other documents in our corpus. We hold 2500 examples for each of the validation and test sets.
Example Output: What is the size of the corpus?

Example Input: We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29.
Example Output: Is this a span-based (extractive) QA task?

Example Input: Spoken-SQuAD is chosen as the target domain data for training and testing. Spoken-SQuAD BIBREF5 is an automatically generated corpus in which the document is in spoken form and the question is in text form. The reference transcriptions are from SQuAD BIBREF1 . There are 37,111 and 5,351 question answer pairs in the training and testing sets respectively, and the word error rate (WER) of both sets is around 22.7%.

The original SQuAD, Text-SQuAD, is chosen as the source domain data, where only question answering pairs appearing in Spoken-SQuAD are utilized. In our task setting, during training we train the proposed QA model on both Text-SQuAD and Spoken-SQuAD training sets. While in the testing stage, we evaluate the performance on Spoken-SQuAD testing set.
Example Output:
Which datasets did they use for evaluation?