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.

[Q]: We use English translations of the Chinese source texts in the WMT 2017 English–Chinese test set BIBREF18 for all experiments presented in this article:
[A]: What languages do they investigate for machine translation?


[Q]: 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.
[A]: Why does the proposed task a good proxy for the general-purpose sequence to sequence tasks?


[Q]: One of the most popular metrics for QG, BLEU BIBREF21 provides a set of measures to compare automatically generated texts against one or more references. In particular, BLEU-N is based on the count of overlapping n-grams between the candidate and its corresponding reference(s). Therefore, we adopt Self-BLEU, originally proposed by BIBREF33, as a measure of diversity for the generated text sequences.  Therefore, given a question-context pair as input to a QA model, two type of metrics can be computed:

n-gram based score: measuring the average overlap between the retrieved answer and the ground truth.

probability score: the confidence of the QA model for its retrieved answer; this corresponds to the probability of being the correct answer assigned by the QA model to the retrieved answer.
[A]:
What automated metrics authors investigate?