Authors:
Cheng Zhang
and
Jie Wang
Affiliation:
Department of Computer Science, University of Massachusetts, Lowell, MA 01854, U.S.A.
Keyword(s):
Tag-Set-Sequence Learning, Question-answer Pairs, Natural Language Processing.
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 t
ests is encouraging.
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