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

Cheng Zhang, Jie Wang

2022

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.

Paper Citation

in Harvard Style

Zhang C. and Wang J. (2022). Tag-Set-Sequence Learning for Generating Question-answer Pairs. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 138-147. DOI: 10.5220/0011586800003335

in Bibtex Style

@conference{kdir22,
author={Cheng Zhang and Jie Wang},
title={Tag-Set-Sequence Learning for Generating Question-answer Pairs},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={138-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011586800003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Tag-Set-Sequence Learning for Generating Question-answer Pairs
SN - 978-989-758-614-9
AU - Zhang C.
AU - Wang J.
PY - 2022
SP - 138
EP - 147
DO - 10.5220/0011586800003335
PB - SciTePress