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3241 results in Artificial Intelligence and Natural Language Processing

Page 11 of 163

  • Preface: Special issue on NLP approaches to offensive content online

  • Marcos Zampieri, Isabelle Augenstein, Siddharth Krishnan, Joshua Melton, Preslav Nakov
  • Journal:
    Natural Language Engineering / Volume 29 / Issue 6 / November 2023
    Published online by Cambridge University Press:
    06 December 2023, p. 1415
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  • We are delighted to present the Special Issue on NLP Approaches to Offensive Content Online published in the Journal of Natural Language Engineering issue 29.6. We are happy to have received a total of 26 submissions to the special issue evidencing the interest of the NLP community in this topic. Our guest editorial board comprised of international experts in the field has worked hard to review all submissions over multiple rounds of peer review. Ultimately, we accepted nine articles to appear in this special issue.

  • OffensEval 2023: Offensive language identification in the age of Large Language Models

  • Marcos Zampieri, Sara Rosenthal, Preslav Nakov, Alphaeus Dmonte, Tharindu Ranasinghe
  • Journal:
    Natural Language Engineering / Volume 29 / Issue 6 / November 2023
    Published online by Cambridge University Press:
    06 December 2023, pp. 1416-1435
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  • The OffensEval shared tasks organized as part of SemEval-2019–2020 were very popular, attracting over 1300 participating teams. The two editions of the shared task helped advance the state of the art in offensive language identification by providing the community with benchmark datasets in Arabic, Danish, English, Greek, and Turkish. The datasets were annotated using the OLID hierarchical taxonomy, which since then has become the de facto standard in general offensive language identification research and was widely used beyond OffensEval. We present a survey of OffensEval and related competitions, and we discuss the main lessons learned. We further evaluate the performance of Large Language Models (LLMs), which have recently revolutionalized the field of Natural Language Processing. We use zero-shot prompting with six popular LLMs and zero-shot learning with two task-specific fine-tuned BERT models, and we compare the results against those of the top-performing teams at the OffensEval competitions. Our results show that while some LMMs such as Flan-T5 achieve competitive performance, in general LLMs lag behind the best OffensEval systems.

  • Data-to-text generation using conditional generative adversarial with enhanced transformer

  • Elham Seifossadat, Hossein Sameti
  • Journal:
    Natural Language Engineering / Volume 30 / Issue 4 / July 2024
    Published online by Cambridge University Press:
    28 November 2023, pp. 696-721
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  • In this paper, we propose an enhanced version of the vanilla transformer for data-to-text generation and then use it as the generator of a conditional generative adversarial model to improve the semantic quality and diversity of output sentences. Specifically, by adding a diagonal mask matrix to the attention scores of the encoder and using the history of the attention weights in the decoder, this enhanced version of the vanilla transformer prevents semantic defects in the output text. Also, using this enhanced transformer along with a triplet network, respectively, as the generator and discriminator of conditional generative adversarial network, diversity and semantic quality of sentences are guaranteed. To prove the effectiveness of the proposed model, called conditional generative adversarial with enhanced transformer (CGA-ET), we performed experiments on three different datasets and observed that our proposed model is able to achieve better results than the baselines models in terms of BLEU, METEOR, NIST, ROUGE-L, CIDEr, BERTScore, and SER automatic evaluation metrics as well as human evaluation.

  • 1 - Introduction to Privacy-preserving Computing

  • Kai Chen, Hong Kong University of Science and Technology, Qiang Yang, WeBank and Hong Kong University of Science and Technology
  • Book:
    Privacy-preserving Computing
    Published online:
    26 October 2023
    Print publication:
    16 November 2023, pp 1-12
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Page 11 of 163