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Enhancing Coherence and Diversity in Multi-class Slogan Generation Systems

Published: 07 August 2024 Publication History

Abstract

Many problems related to natural language processing are solved by neural networks and big data. Researchers have previously focused on single-task supervised goals with limited data management to train slogan classification. A multi-task learning framework is used to learn jointly across several tasks related to generating multi-class slogan types. This study proposes a multi-task model named slogan generative adversarial network systems (Slo-GAN) to enhance coherence and diversity in slogan generation, utilizing generative adversarial networks and recurrent neural networks (RNN). Slo-GAN generates a new text slogan-type corpus, and the training generalization process is improved. We explored active learning (AL) and meta-learning (ML) for dataset labeling efficiency. AL reduced annotations by 10% compared to ML but still needed about 70% of the full dataset for baseline performance. The whole framework of Slo-GAN is supervised and trained together on all of these tasks. The text with the higher reporting score level is filtered by Slo-GAN, and a classification accuracy of 87.2% is achieved. We leveraged relevant datasets to perform a cross-domain experiment, reinforcing our assertions regarding both the distinctiveness of our dataset and the challenges of adapting bilingual dialects to one another.

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  • (2025)Hierarchical multi-label text classification of tourism resources using a label-aware dual graph attention networkInformation Processing & Management10.1016/j.ipm.2024.10395262:1(103952)Online publication date: Jan-2025
  • (2024)Semantic web-based propaganda text detection from social media using meta-learningService Oriented Computing and Applications10.1007/s11761-024-00422-xOnline publication date: 2-Aug-2024
  • (2024)LegalATLE: an active transfer learning framework for legal triple extractionApplied Intelligence10.1007/s10489-024-05842-yOnline publication date: 2-Oct-2024

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  1. Enhancing Coherence and Diversity in Multi-class Slogan Generation Systems

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    cover image ACM Transactions on Asian and Low-Resource Language Information Processing
    ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 23, Issue 8
    August 2024
    343 pages
    EISSN:2375-4702
    DOI:10.1145/3613611
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 August 2024
    Online AM: 15 December 2023
    Accepted: 12 December 2023
    Revised: 10 November 2023
    Received: 20 September 2023
    Published in TALLIP Volume 23, Issue 8

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    Author Tags

    1. Generation
    2. big data
    3. Slo-GAN
    4. RNN
    5. classification
    6. neural networks
    7. multi-class

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    • National Natural Science Foundation of China

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    • (2025)Hierarchical multi-label text classification of tourism resources using a label-aware dual graph attention networkInformation Processing & Management10.1016/j.ipm.2024.10395262:1(103952)Online publication date: Jan-2025
    • (2024)Semantic web-based propaganda text detection from social media using meta-learningService Oriented Computing and Applications10.1007/s11761-024-00422-xOnline publication date: 2-Aug-2024
    • (2024)LegalATLE: an active transfer learning framework for legal triple extractionApplied Intelligence10.1007/s10489-024-05842-yOnline publication date: 2-Oct-2024

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