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: We acquired the data in two rounds of annotation. In the first one, we were looking for original and uncommon sentence change suggestions.
Example Output: How do they introduce language variation?

Example Input: Further, FSDM has a new module called response slot binary classifier that adds extra supervision to generate the slots that will be present in the response more precisely before generating the final textual agent response (see Section "Methodology" for details).
Example Output: How do slot binary classifiers improve performance?

Example Input: Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates.  The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations.  Our abstractor is practically identical to the one proposed in BIBREF8.
Example Output:
What's the method used here?