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    <abstract>The current framework for sequence labeling encompasses a feature extractor and a sequence tagger. This study introduces a unified framework named SLGAN, which harnesses the capabilities of Generative Adversarial Networks to address the challenges associated with Sequence Labeling tasks. SLGAN not only mitigates the limitation of GANs in backpropagating loss to discrete data but also exhibits strong adaptability to various sequence labeling tasks. Unlike traditional GANs, the discriminator within SLGAN does not discriminate whether data originates from the discriminator or the generator; instead, it focuses on predicting the correctness of each tag within the tag sequence. We conducted evaluations on six different tasks spanning four languages, including Chinese, Japanese, and Korean Word Segmentation, Chinese and English Named Entity Recognition, and Chinese Part-of-Speech Tagging. Our experimental results illustrate that SLGAN represents a versatile and highly effective solution, consistently achieving state-of-the-art or competitive performance results, irrespective of the specific task or language under consideration.</abstract>
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%0 Conference Proceedings
%T When Generative Adversarial Networks Meet Sequence Labeling Challenges
%A Tong, Yu
%A Chen, Ge
%A Zheng, Guokai
%A Li, Rui
%A Dazhi, Jiang
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F tong-etal-2024-generative
%X The current framework for sequence labeling encompasses a feature extractor and a sequence tagger. This study introduces a unified framework named SLGAN, which harnesses the capabilities of Generative Adversarial Networks to address the challenges associated with Sequence Labeling tasks. SLGAN not only mitigates the limitation of GANs in backpropagating loss to discrete data but also exhibits strong adaptability to various sequence labeling tasks. Unlike traditional GANs, the discriminator within SLGAN does not discriminate whether data originates from the discriminator or the generator; instead, it focuses on predicting the correctness of each tag within the tag sequence. We conducted evaluations on six different tasks spanning four languages, including Chinese, Japanese, and Korean Word Segmentation, Chinese and English Named Entity Recognition, and Chinese Part-of-Speech Tagging. Our experimental results illustrate that SLGAN represents a versatile and highly effective solution, consistently achieving state-of-the-art or competitive performance results, irrespective of the specific task or language under consideration.
%R 10.18653/v1/2024.emnlp-main.593
%U https://aclanthology.org/2024.emnlp-main.593/
%U https://doi.org/10.18653/v1/2024.emnlp-main.593
%P 10625-10635

Markdown (Informal)

[When Generative Adversarial Networks Meet Sequence Labeling Challenges](https://aclanthology.org/2024.emnlp-main.593/) (Tong et al., EMNLP 2024)

• When Generative Adversarial Networks Meet Sequence Labeling Challenges (Tong et al., EMNLP 2024)

ACL

• Yu Tong, Ge Chen, Guokai Zheng, Rui Li, and Jiang Dazhi. 2024. When Generative Adversarial Networks Meet Sequence Labeling Challenges. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 10625–10635, Miami, Florida, USA. Association for Computational Linguistics.