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

• URL: http://arxiv.org/abs/2210.11608v1
• Date: Thu, 20 Oct 2022 21:51:00 GMT
• Title: Tag-Set-Sequence Learning for Generating Question-Answer Pairs
• Authors: Cheng Zhang, Jie Wang
• Abstract summary: We present a new method called tag-set sequence learning to tackle the problem of generating silly questions for texts. We construct a system called TSS-Learner to learn tag-set sequences from given declarative sentences and the corresponding interrogative sentences. We show that TSS-Learner can indeed generate adequate QAPs for certain texts that transformer-based models do poorly.
• Score: 10.48660454637293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based QG models can generate question-answer pairs (QAPs) with high qualities, but may also generate silly questions for certain texts. We present 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 and semantic information of the underlying sentence, and a tag set consists of one or more language feature tags, including, for example, semantic-role-labeling, part-of-speech, named-entity-recognition, and sentiment-indication tags. We construct a system called TSS-Learner to learn tag-set sequences from given declarative sentences and the corresponding interrogative sentences, and derive answers to the latter. We train a TSS-Learner model for the English language using a small training dataset and show that it can indeed generate adequate QAPs for certain texts that transformer-based models do poorly. Human evaluation on the QAPs generated by TSS-Learner over SAT practice reading tests is encouraging.

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