@inproceedings{huang-etal-2023-diffusionsl,
title = "{D}iffusion{SL}: Sequence Labeling via Tag Diffusion Process",
author = "Huang, Ziyang and
Cao, Pengfei and
Zhao, Jun and
Liu, Kang",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.860",
doi = "10.18653/v1/2023.findings-emnlp.860",
pages = "12902--12920",
abstract = "Sequence Labeling (SL) is long-standing in Natural Language Processing (NLP). Traditionally, discriminative models have been widely used to capture the conditional distribution of sequence tags, rather than generative models. In this paper, we present DiffusionSL, a framework that utilizes a conditional discrete diffusion model for generating discrete tag data, resulting in a Tag Diffusion Process. We treat the natural language sequence as the conditional signal and the sequence tags as the generation target, iteratively refining the noisy tags to obtain clean ones. To address the discreteness issue, we propose the Bit-Tag Converter (BTConverter) to model the target in continuous data space. Furthermore, we introduce the Bit Diffusion Transformer (BitDiT) to model the process of noise elimination. Leveraging the powerful iterative refinement capability of the diffusion model, DiffusionSL achieves superior performance against previous state-of-the-art (SOTA) baselines and outperforms gpt-3.5-turbo significantly across multiple benchmark datasets and various tasks.",
}
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<abstract>Sequence Labeling (SL) is long-standing in Natural Language Processing (NLP). Traditionally, discriminative models have been widely used to capture the conditional distribution of sequence tags, rather than generative models. In this paper, we present DiffusionSL, a framework that utilizes a conditional discrete diffusion model for generating discrete tag data, resulting in a Tag Diffusion Process. We treat the natural language sequence as the conditional signal and the sequence tags as the generation target, iteratively refining the noisy tags to obtain clean ones. To address the discreteness issue, we propose the Bit-Tag Converter (BTConverter) to model the target in continuous data space. Furthermore, we introduce the Bit Diffusion Transformer (BitDiT) to model the process of noise elimination. Leveraging the powerful iterative refinement capability of the diffusion model, DiffusionSL achieves superior performance against previous state-of-the-art (SOTA) baselines and outperforms gpt-3.5-turbo significantly across multiple benchmark datasets and various tasks.</abstract>
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%0 Conference Proceedings
%T DiffusionSL: Sequence Labeling via Tag Diffusion Process
%A Huang, Ziyang
%A Cao, Pengfei
%A Zhao, Jun
%A Liu, Kang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F huang-etal-2023-diffusionsl
%X Sequence Labeling (SL) is long-standing in Natural Language Processing (NLP). Traditionally, discriminative models have been widely used to capture the conditional distribution of sequence tags, rather than generative models. In this paper, we present DiffusionSL, a framework that utilizes a conditional discrete diffusion model for generating discrete tag data, resulting in a Tag Diffusion Process. We treat the natural language sequence as the conditional signal and the sequence tags as the generation target, iteratively refining the noisy tags to obtain clean ones. To address the discreteness issue, we propose the Bit-Tag Converter (BTConverter) to model the target in continuous data space. Furthermore, we introduce the Bit Diffusion Transformer (BitDiT) to model the process of noise elimination. Leveraging the powerful iterative refinement capability of the diffusion model, DiffusionSL achieves superior performance against previous state-of-the-art (SOTA) baselines and outperforms gpt-3.5-turbo significantly across multiple benchmark datasets and various tasks.
%R 10.18653/v1/2023.findings-emnlp.860
%U https://aclanthology.org/2023.findings-emnlp.860
%U https://doi.org/10.18653/v1/2023.findings-emnlp.860
%P 12902-12920
Markdown (Informal)
[DiffusionSL: Sequence Labeling via Tag Diffusion Process](https://aclanthology.org/2023.findings-emnlp.860) (Huang et al., Findings 2023)
ACL