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Transformer-based QG models can generate question-answer pairs (QAPs) withhigh qualities, but may also generate silly questions for certain texts. Wepresent a new method called tag-set sequence learning to tackle this problem,where a tag-set sequence is a sequence of tag sets to capture the syntactic andsemantic information of the underlying sentence, and a tag set consists of oneor more language feature tags, including, for example, semantic-role-labeling,part-of-speech, named-entity-recognition, and sentiment-indication tags. Weconstruct a system called TSS-Learner to learn tag-set sequences from givendeclarative sentences and the corresponding interrogative sentences, and deriveanswers to the latter. We train a TSS-Learner model for the English languageusing a small training dataset and show that it can indeed generate adequateQAPs for certain texts that transformer-based models do poorly. Humanevaluation on the QAPs generated by TSS-Learner over SAT practice reading testsis encouraging.

Tag-Set-Sequence Learning for Generating Question-Answer Pairs