Improving Long Content Question Generation with Multi-level Passage Encoding

Peide Zhu*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Generating questions that can be answered with word spans from passages is an important natural language task, which can be used for educational applications, question-answering systems, and conversational systems. Existing question generation models suffer from creating questions that are often unrelated to the context passage and answer span. In this paper, we first analyze questions generated by a common baseline model: we find over half of the generated questions that are rated as the lowest quality to be semantically unrelated to the context passage. We then investigate how humans ask factual questions and show that most often they are a reformulation of the target sentence and information from context passage. Based on these findings, we propose a multi-level encoding and gated attention fusion based neural network model for question generation (QG) which overcomes these shortcomings. Our experiments demonstrate that our model outperforms existing state-of-art seq2seq QG models.

Original languageEnglish
Title of host publicationPRICAI 2021
Subtitle of host publicationTrends in Artificial Intelligence - 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Proceedings
EditorsDuc Nghia Pham, Thanaruk Theeramunkong, Guido Governatori, Fenrong Liu
Place of PublicationCham
PublisherSpringer
Pages140-152
Number of pages13
EditionPart II
ISBN (Electronic)978-3-030-89363-7
ISBN (Print)978-3-030-89362-0
DOIs
Publication statusPublished - 2021
Event18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021 - Virtual, Online
Duration: 8 Nov 202112 Nov 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume13032
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021
CityVirtual, Online
Period8/11/2112/11/21

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