Papers with QA training

2 papers
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering (2020.acl-main)

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Challenge: Question Answering (QA) is a field of increasing demand due to the availability of information online.
Approach: They propose an unsupervised approach to training QA models with generated pseudo-training data by applying a simple template on a related sentence rather than the original context sentence.
Outcome: The proposed approach improves the performance of a QA model on generated pseudo-training data.
On the Importance of Diversity in Question Generation for QA (2020.acl-main)

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Challenge: In this paper, we show that textual diversity in automatic question generation is beneficial for downstream QA.
Approach: They propose to use textual diversity to promote automatic question generation as a quality measure for QA.
Outcome: The proposed measure of QG quality correlates well with evaluation on QA.

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