research-article

Neural Question Generation based on Seq2Seq

Author:

Bingran LiuAuthors Info & Claims

ICMAI '20: Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence

Pages 119 - 123

https://doi.org/10.1145/3395260.3395275

Published: 29 May 2020 Publication History

Abstract

Neural Question Generation is the use of deep neural networks to extract target answers from a given article or paragraph and generate questions based on the target answers. There is a problem in the previous NQG(Neural Question Generation) model, and the generated question does not explicitly connect with the context in the target answer, resulting in a large part of the generated question containing the target answer and the accuracy is not high. In this paper, a QG model based on seq2seq is used, which consists of encode and decoder, and adds the attention mechanism and copy mechanism. We use special tags to replace the target answer of the original paragraph, and use the paragraph and target answer as input to reduce the number of incorrect questions, including the correct answer. Through the partial copy mechanism based on character overlap, we can make the generation problem have higher overlap and relevance at the word level and the input document. Experiments show that our proposed model performs better than before.

References

[1]

Heilman, M., and Smith, N. A.2010. Good question! statistical ranking for question generation. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 609--617. Association for Computational Linguistics.

[2]

Iulian Vlad Serban, Alberto Garc´ la-Durán, Caglar Gulcehre, Sungjin Ahn, Sarath Chandar, Aaron Courville, and Yoshua Bengio. 2016. Generating factoid questions with recurrent neural networks: The 30M factoid question-answer corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 588--598, Berlin, Germany. Association for Computational Linguistics.

[3]

Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, and Lucy Vanderan image. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1: Long Papers), pages 1802--1813, Berlin, Germany. Association for Computational Linguistics.

[4]

Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, and Shi Wang. 2018. Answer-focused and position-aware neural question generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3930--3939, Brussels, Belgium. Association for Computational Linguistics.z3

[5]

Duan, N.; Tang, D.; Chen, P.; and Zhou, M. 2017. Question generation for question answering. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 866--874.

[6]

Tang, D.; Duan, N.; Qin, T.; Yan, Z.; and Zhou, M. 2017. Question answering and question generation as dual tasks. arXiv preprint arXiv:1706.02027.

[7]

Tang, D.; Duan, N.; Yan, Z.; Zhang, Z.; Sun, Y.; Liu, S.; Lv, Y.; and Zhou, M. 2018. Learning to collaborate for question answering and asking. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), volume 1, 1564--1574.

[8]

Mostafazadeh, N.; Misra, I.; Devlin, J.; Mitchell, M.; He, X.; and Vanderwende, L. 2016.Generating natural questions about an image. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, 1802--1813.

[9]

Du, X.; Shao, J.; and Cardie, C. 2017. Learning to ask: Neural question generation for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, 1342 -1352.

[10]

Sutskever, I.; Vinyals, O.;andLe, Q.V. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, 3104--3112.

[11]

Bahdanau, D.; Cho, K.;and Bengio, Y. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations.

[12]

Serban, I. V.; Sordoni, A.; Lowe, R.;Charlin, L.; Pineau, J.; Courville, A. C.; and Bengio, Y.2017. A hierarchical latent variable encoder-decoder model for generating dialogues. In AAAI, 3295--3301.

[13]

Zhou, Q.; Yang, N.; Wei, F.; Tan, C.; Bao, H.; and Zhou, M. 2017. Neural question generation from text: A preliminary study. In National CCF Conference on Natural Language Processing and Chinese Computing, 662 -671. Springer.

[14]

Pranav Rajpurkar, JianZhang, KonstantinLopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383--2392, Austin, Texas. Association for Computational Linguistics.

[15]

Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, and Shi Wang. 2018. Answer-focused and position-aware neural question generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3930 -3939, Brussels, Belgium. Association for Computational Linguistics.

[16]

Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc ' Aurelio Ranzato, and Jason Weston. 2017.Learning through dialogue interactions by asking questions. In ICLR.

[17]

Siyuan Wang, Zhongyu Wei, Zhihao Fan, Yang Liu, and Xuanjing Huang. 2019. A multi-agent communication framework for question-worthy phrase extraction and question generation. In AAAI Conference on Artificial Intelligence.

[18]

Ma, S.; Sun, X.; Li, W.; Li, S.; Li, W.;and Ren, X. 2018. Query and output: Generating words by querying distributed word representations for paraphrase generation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), volume 1, 196--2

Cited By

Hu JGuo JTang NMa XYao YYang CXu Y(2024)Designing the Conversational Agent: Asking Follow-up Questions for Information ElicitationProceedings of the ACM on Human-Computer Interaction10.1145/36373208:CSCW1(1-30)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637320
Xu JZhang LSun YWang JDuan S(2024)Personalized Questioning Teaching Mode for English Reading in Junior High School Based on Automatic Question GenerationComputer Science and Educational Informatization10.1007/978-981-99-9492-2_9(90-102)Online publication date: 10-Jan-2024
https://doi.org/10.1007/978-981-99-9492-2_9
Xu JSun YGan JZhou MWu D(2023)Leveraging Structured Information from a Passage to Generate QuestionsTsinghua Science and Technology10.26599/TST.2022.901003428:3(464-474)Online publication date: Jun-2023
https://doi.org/10.26599/TST.2022.9010034
Show More Cited By

Index Terms

Neural Question Generation based on Seq2Seq
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

Recommendations

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
WWW '20: Proceedings of The Web Conference 2020

The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation ...
Neural Question Generation with Semantics of Question Type
Natural Language Processing and Chinese Computing
Abstract
This paper focuses on automatic question generation (QG) that transforms a narrative sentence into an interrogative sentence. Recently, neural networks have been used in this task due to its extraordinary ability of semantics encoding and ...
Graph augmented sequence-to-sequence model for neural question generation
Abstract
Neural question generation (NQG) aims to generate a question from a given passage with neural networks. NQG has attracted more attention in recent years, due to its wide applications in reading comprehension, question answering, and dialogue ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMAI '20: Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence

April 2020

252 pages

ISBN:9781450377072

DOI:10.1145/3395260

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Southwest Jiaotong University
Xihua University: Xihua University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICMAI 2020

ICMAI 2020: 2020 5th International Conference on Mathematics and Artificial Intelligence

April 10 - 13, 2020

Chengdu, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
236
Total Downloads

Downloads (Last 12 months)30
Downloads (Last 6 weeks)2

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hu JGuo JTang NMa XYao YYang CXu Y(2024)Designing the Conversational Agent: Asking Follow-up Questions for Information ElicitationProceedings of the ACM on Human-Computer Interaction10.1145/36373208:CSCW1(1-30)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637320
Xu JZhang LSun YWang JDuan S(2024)Personalized Questioning Teaching Mode for English Reading in Junior High School Based on Automatic Question GenerationComputer Science and Educational Informatization10.1007/978-981-99-9492-2_9(90-102)Online publication date: 10-Jan-2024
https://doi.org/10.1007/978-981-99-9492-2_9
Xu JSun YGan JZhou MWu D(2023)Leveraging Structured Information from a Passage to Generate QuestionsTsinghua Science and Technology10.26599/TST.2022.901003428:3(464-474)Online publication date: Jun-2023
https://doi.org/10.26599/TST.2022.9010034
Goyal RKumar PSingh V(2023)Automated Question and Answer Generation from Texts using Text-to-Text TransformersArabian Journal for Science and Engineering10.1007/s13369-023-07840-7Online publication date: 3-May-2023
https://doi.org/10.1007/s13369-023-07840-7
Santi MManacero APeronaglio FLobato RSpolon RCavenaghi M(2022)Training Transformers for Question Generation Task in Intelligent Tutoring Systems2022 17th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI54924.2022.9820606(1-6)Online publication date: 22-Jun-2022
https://doi.org/10.23919/CISTI54924.2022.9820606
BULUT OYİLDİRİM-ERBASLİ S(2022)Automatic story and item generation for reading comprehension assessments with transformersAutomatic story and item generation for reading comprehension assessments with transformersInternational Journal of Assessment Tools in Education10.21449/ijate.11243829:Special Issue(72-87)Online publication date: 29-Nov-2022
https://doi.org/10.21449/ijate.1124382
Toshevska MGievska S(2022)A Review of Text Style Transfer Using Deep LearningIEEE Transactions on Artificial Intelligence10.1109/TAI.2021.31159923:5(669-684)Online publication date: Oct-2022
https://doi.org/10.1109/TAI.2021.3115992
Phan TDo P(2022)NER2QUES: combining named entity recognition and sequence to sequence to automatically generating Vietnamese questionsNeural Computing and Applications10.1007/s00521-021-06477-734:2(1593-1612)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s00521-021-06477-7

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten