skip to main content
10.1145/3495018.3501202acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaiamConference Proceedingsconference-collections
research-article

Short Text Generation Based on Adversarial Graph Attention Networks

Published: 14 March 2022 Publication History

Abstract

Text generation has attracted more and more attention in the field of natural language. Recently, GAN (Generative Adversarial Networks) have been widely used in text generation, among which the GAN-based models, such as SeqGAN and SentiGAN, have shown remarkable effects in text generation. However, previous text generation models simply use CNN (Convolutional Neural Networks) as discriminators and ignore relationships between the same-label texts. Meanwhile, most models only consider using a single generator to generate a single species text, not for multispecies texts. To meet the requirements, in this paper, we propose a novel framework model-SGATGAN, which applies GAT (Generative Attention Nets) as the discriminator to establish the connection between the texts of the same type. It also provides a method of generating multispecies texts using a single generator. In this model, the graph attention neural network is used as the discriminator via the feedback to guide the generator in a specific location to generate a specific type of short text. Experimental results on two benchmarks show that our model significantly outperforms previous methods, giving state-of-the-art results in short text generation.

References

[1]
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27, 2672-2680.Yu L,
[2]
Yu, L., Zhang, W., Wang, J., & Yu, Y. (2017, February). SeqGAN: Sequence generative adversarial nets with policy gradient. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31, No. 1).
[3]
Wang, K., & Wan, X. (2018, July). SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks. In IJCAI (pp. 4446-4452).
[4]
Lin K, LiD, He x Adversarial ranking for language generation[C]. Advances in Neural Information Processing Systems, 2017: 3155-3165.
[5]
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.
[6]
Cheng, J., Dong, L., and Lapata, M. Long short-term memory-networks for machine reading. In EMNLP, 2016.
[7]
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27, 3104-3112.
[8]
Pham, H., Manzini, T., Liang, P. P., & Poczos, B. (2018). Seq2seq2 sentiment: Multimodal sequence to sequence models for sentiment analysis. arXiv preprint arXiv:1807.03915.
[9]
Cheng, F., & Miyao, Y. (2017, July). Classifying temporal relations by bidirectional lstm over dependency paths. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 1-6).
[10]
Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016.
[11]
Yao, L., Mao, C., & Luo, Y. (2019, July). Graph convolutional networks for text classification. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 7370-7377).
[12]
Li, Y., Tarlow, D., Brockschmidt, M., & Zemel, R. (2015). Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493.
[13]
Plecháč, P. (2019). Relative contributions of Shakespeare and Fletcher in Henry VIII: An Analysis Based on Most Frequent Words and Most Frequent Rhythmic Patterns. arXiv preprint arXiv:1911.05652.
[14]
Andresen, M., & Zinsmeister, H. (2017, September). The benefit of syntactic vs. linear n-grams for linguistic description. In Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017) (pp. 4-14).
[15]
Koch K R. Maximum likelihood estimate of variance components[J]. Bulletin Gæodésique, 1986, 60(4): 329-338.
[16]
Grassberger P. Go with the winners: A general Monte Carlo strategy[J]. Computer Physics Communications, 2002, 147(1-2): 64-70.
[17]
Yao L, Mao C, Luo Y. Graph convolutional networks for text classification[C]//Proceedings of the AAAI conference on artificial intelligence. 2019, 33(01): 7370-7377.
[18]
Silver D, Lever G, Heess N, Deterministic policy gradient algorithms[C]//International conference on machine learning. PMLR, 2014: 387-395.
[19]
Papineni K, Roukos S, Ward T, Bleu: a method for automatic evaluation of machine translation[C]//Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 2002: 311-318.
[20]
Yin H, Li D, Li X, Meta-cotgan: A meta cooperative training paradigm for improving adversarial text generation[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(05): 9466-9473.

Cited By

View all
  • (2024)Learning Section Weights for Multi-label Document ClassificationNatural Language Processing and Information Systems10.1007/978-3-031-70242-6_34(359-366)Online publication date: 20-Sep-2024
  • (2023)Adversarial Text Perturbation Generation and Analysis2023 3rd Intelligent Cybersecurity Conference (ICSC)10.1109/ICSC60084.2023.10349981(67-73)Online publication date: 23-Oct-2023
  1. Short Text Generation Based on Adversarial Graph Attention Networks

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
    October 2021
    3136 pages
    ISBN:9781450385046
    DOI:10.1145/3495018
    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]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 March 2022

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    AIAM2021

    Acceptance Rates

    Overall Acceptance Rate 100 of 285 submissions, 35%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)11
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 30 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Learning Section Weights for Multi-label Document ClassificationNatural Language Processing and Information Systems10.1007/978-3-031-70242-6_34(359-366)Online publication date: 20-Sep-2024
    • (2023)Adversarial Text Perturbation Generation and Analysis2023 3rd Intelligent Cybersecurity Conference (ICSC)10.1109/ICSC60084.2023.10349981(67-73)Online publication date: 23-Oct-2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media