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Creating multiple-choice questions to assess reading comprehension of a givenarticle involves generating question-answer pairs (QAPs) on the main points ofthe document. We present a learning scheme to generate adequate QAPs viameta-sequence representations of sentences. A meta sequence is a sequence ofvectors comprising semantic and syntactic tags. In particular, we devise ascheme called MetaQA to learn meta sequences from training data to form pairsof a meta sequence for a declarative sentence (MD) and a correspondinginterrogative sentence (MIs). On a given declarative sentence, a trained MetaQAmodel converts it to a meta sequence, finds a matched MD, and uses thecorresponding MIs and the input sentence to generate QAPs. We implement MetaQAfor the English language using semantic-role labeling, part-of-speech tagging,and named-entity recognition, and show that trained on a small dataset, MetaQAgenerates efficiently over the official SAT practice reading tests a largenumber of syntactically and semantically correct QAPs with over 97\% accuracy.

Meta Sequence Learning for Generating Adequate Question-Answer Pairs