| Challenge: | Sequence Labeling (SL) is a long-standing field of natural language processing. |
| Approach: | They propose a framework that utilizes a conditional discrete diffusion model for generating discrete tag data. |
| Outcome: | The proposed framework outperforms gpt-3.5-turbo on multiple benchmark datasets and tasks. |
Similar Papers
Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
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| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
| Approach: | They propose to use pretrained Transformer encoders to model relations across words . they find that the architectures adapt well across tagging tasks that vary in complexity . |
| Outcome: | The proposed architectures perform well across tagging tasks across languages and datasets. |
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)
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| Challenge: | Existing sequence labeling algorithms can be decomposed into two parts . |
| Approach: | They propose a graph neural networks sequence labeling (GNN-SL) that augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. |
| Outcome: | The proposed model performs well on three sequence labeling tasks. |
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation (2024.eacl-long)
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| Challenge: | Existing non-autoregressive (NAR) text-to-text generation methods are unable to generate coherent and fluent texts due to discrete nature of text. |
| Approach: | They propose to integrate discrete diffusion models (DDM) into NAR text-to-text generation and integrate BART to improve the performance. |
| Outcome: | The proposed method outperforms competing methods and surpasses autoregressive methods on 7 datasets. |
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (P19-1)
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| Challenge: | Existing systems for sequence labeling are limited by shallow connections between consecutive hidden states and insufficient modeling of global information. |
| Approach: | They propose a global context enhanced deep transition architecture for sequence labeling . they deepen the state transition path at each position in a sentence and assign tokens with global representations . |
| Outcome: | The proposed architecture outperforms the best reported results on two standard sequence labeling tasks. |
When Generative Adversarial Networks Meet Sequence Labeling Challenges (2024.emnlp-main)
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| Challenge: | Existing approaches for sequence labeling use a feature extractor and sequence tagger . a recent study shows that SLGAN is versatile and highly effective . |
| Approach: | They propose a framework that harnesses the capabilities of Generative Adversarial Networks to address sequence labeling challenges. |
| Outcome: | The proposed framework exhibits strong adaptability to various sequence labeling tasks. |
Small and Practical BERT Models for Sequence Labeling (D19-1)
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| Challenge: | Existing models for morphosyntactic tagging have focused on building separate models for each language or for a small group of related languages. |
| Approach: | They propose a scheme to train a single multilingual sequence labeling model that is small and fast enough to run on a CPU. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages and low-level models on codemixed inputs. |
Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning (2024.naacl-long)
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| Challenge: | Experimental results show that non-autoregressive generation models are superior in generation efficiency but inferior in generation quality. |
| Approach: | They propose a diffusion glancing transformer which employs a modality diffusion process and residual glancy sampling to improve multi-modality modeling. |
| Outcome: | The proposed model outperforms autoregressive and non-autoregressive models on machine translation and text generation benchmarks. |
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models (2025.acl-long)
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| Challenge: | Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text. |
| Approach: | They propose a framework that enhances diffusion-based text generation through text segmentation, robust representation training with adversarial and contrastive learning, and improved latent-space guidance. |
| Outcome: | The proposed framework improves diffusion-based text generation and improves scalability and fluency. |
SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)
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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou
| Challenge: | Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling. |
| Approach: | They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation. |
| Outcome: | The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes. |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling (2020.acl-main)
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| Challenge: | Sequence labeling systems should perform reliably under ideal conditions and with corrupted inputs. |
| Approach: | They propose two noise-aware training objectives that improve robustness of sequence labeling performed on perturbed inputs. |
| Outcome: | The proposed methods improve robustness on English and German named entity recognition benchmarks. |