Reviving Iterative Refinement in Diffusion-based NER with an Initializer-Restorer Approach

Challenge:	Named Entity Recognition (NER) is a fundamental task in Information Extraction.
Approach:	They propose a generative paradigm for Named Entity Recognition by modeling NER as a boundary diffusion process.
Outcome:	The proposed model performs better than baseline on ACE2004, GENIA, and CleanCoNLL.

Similar Papers

Overview

Challenge

Approach

Outcome

DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)

Copied to clipboard

Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang

Information Extraction & Text Mining

Challenge:	Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks.
Approach:	They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans.
Outcome:	The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets.

Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)

Copied to clipboard

Yun Hu, Yeshuang Zhu, Jinchao Zhang, Changwen Zheng, Jie Zhou

Language Models

Challenge:	Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing.
Approach:	They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances.
Outcome:	Empirical results show that the proposed model improves against baselines and can be scaled to a large extent.

A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages (2026.findings-eacl)

Copied to clipboard

Toqeer Ehsan, Thamar Solorio

Information Extraction & Text Mining

Challenge:	Existing NER benchmarks lack quality annotations, resulting in poor performance.
Approach:	They propose a frequency-based iterative approach that leverages self-training and a dual-threshold mechanism to enhance inference confidence.
Outcome:	The proposed approach improves NER performance on three datasets with a high number of missing annotations.

DiffusionRet: Diffusion-Enhanced Generative Retriever using Constrained Decoding (2023.findings-emnlp)

Copied to clipboard

Shanbao Qiao, Xuebing Liu, Seung-Hoon Na

Information Retrieval

Challenge:	Generative retrieval methods have suffered from the lack of the intermediate reasoning step . generative retrieval uses sequence-to-sequence diffusion models to map a query to relevant docids .
Approach:	They propose a novel method that uses query as an intermediate step before retrieval . they propose to use sequence-to-sequence diffusion models to map a query to relevant docids .
Outcome:	Experiments show that proposed method outperforms existing methods on MARCO and Natural Questions datasets.

Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples (2022.findings-acl)

Copied to clipboard

Yu Xia, Quan Wang, Yajuan Lyu, Yong Zhu, Wenhao Wu, Sujian Li, Dai Dai

Language Models

Challenge:	Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved.
Approach:	They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion.
Outcome:	The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0.

ISR: Self-Refining Referring Expressions for Entity Grounding (2025.acl-long)

Copied to clipboard

Zhuocheng Yu, Bingchan Zhao, Yifan Song, Sujian Li, Zhonghui He

Multimodality

Challenge:	Entity grounding is a crucial task in the construction of multimodal knowledge graphs.
Approach:	They propose a novel scheme to enhance the multimodal large language model's capability to generate high quality REs for the given entities as explicit contextual clues.
Outcome:	The proposed method surpasses other methods in entity grounding, highlighting its effectiveness, robustness and potential for broader applications.

NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks (2023.acl-industry)

Copied to clipboard

Xinpeng Zhang, Ming Tan, Jingfan Zhang, Wei Zhu

Information Extraction & Text Mining

Challenge:	Existing models for general NER tasks require entities to be generated in a predefined order, causing error propagation and inefficient decoding.
Approach:	They propose a non-autoregressive generation framework for general NER tasks that generates entities as a set instead of a sequence, avoiding error propagation and inefficient decoding.
Outcome:	The proposed model outperforms state-of-the-art models on three benchmark NER datasets and two of our proprietary NER tasks.

Few-Shot Class-Incremental Learning for Named Entity Recognition (2022.acl-long)

Copied to clipboard

Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, Subrata Mitra, Ruiyi Zhang, Ricardo Henao

Low-Resource NLP

Challenge:	Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class.
Approach:	They propose a few-shot class-incremental learning problem where NER model is trained with only few labeled samples of the new classes without forgetting knowledge of the old ones.
Outcome:	The proposed model improves over existing baselines by reconstructing training data of old classes and real data from the training set.

NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)

Copied to clipboard

Uri Katz, Matan Vetzler, Amir Cohen, Yoav Goldberg

Semantic Text Processing

Challenge:	Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them .
Approach:	They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition .
Outcome:	The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs .

SKD-NER: Continual Named Entity Recognition via Span-based Knowledge Distillation with Reinforcement Learning (2023.emnlp-main)

Copied to clipboard

Yi Chen, Liang He

Language Models

Challenge:	Continual learning for named entity recognition (CL-NER) aims to enable models to continuously learn new entity types while retaining the ability to recognize previously learned ones.
Approach:	They propose a model that leverages knowledge distillation to retain memory and employs reinforcement learning strategies to optimize the soft labeling and distillation losses generated by the teacher model to effectively prevent catastrophic forgetting.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it significantly improves the performance of the CL-NER task.