instruction:
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.
question:
The task is set up to mimic (albeit, in an oversimplified manner) the input-output symbol alignments and local syntactic properties that models must learn in many natural language tasks, such as translation, tagging and summarization.
answer:
Why does the proposed task a good proxy for the general-purpose sequence to sequence tasks?

question:
o evaluate the quality of the generated sequences regarding both precision and recall, the evaluation metrics include BLEU and ROUGE (1, 2, L) scores with multiple references BIBREF22 .
answer:
What evaluation metrics are used?

question:
For instance, schema 23 from the WSC collection can be translated into both "The girls were bullying the boys so we [punished/rescued] them" and "The boys were bullying the girls, so we [punished/rescued] them," thus avoiding any presupposition of whether girls are more likely to bully boys or vice versa.
answer:
What data do they look at?