Meta Sequence Learning for Generating Adequate Question-Answer Pairs

• URL: http://arxiv.org/abs/2010.01620v2
• Date: Thu, 30 Sep 2021 14:23:32 GMT
• Title: Meta Sequence Learning for Generating Adequate Question-Answer Pairs
• Authors: Cheng Zhang, Jie Wang
• Abstract summary: We present a learning scheme to generate adequate QAPs via meta-sequence representations of sentences. On a given declarative sentence, a trained MetaQA model converts it to a meta sequence, finds a matched MD, and uses the corresponding MIs and the input sentence to generate QAPs. We show that MetaQA generates efficiently over the official SAT practice reading tests a large number of syntactically and semantically correct QAPs with over 97% accuracy.
• Score: 10.48660454637293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) on the main points of the document. We present a learning scheme to generate adequate QAPs via meta-sequence representations of sentences. A meta sequence is a sequence of vectors comprising semantic and syntactic tags. In particular, we devise a scheme called MetaQA to learn meta sequences from training data to form pairs of a meta sequence for a declarative sentence (MD) and a corresponding interrogative sentence (MIs). On a given declarative sentence, a trained MetaQA model converts it to a meta sequence, finds a matched MD, and uses the corresponding MIs and the input sentence to generate QAPs. We implement MetaQA for the English language using semantic-role labeling, part-of-speech tagging, and named-entity recognition, and show that trained on a small dataset, MetaQA generates efficiently over the official SAT practice reading tests a large number of syntactically and semantically correct QAPs with over 97\% accuracy.

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