Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to sequence labeling are limited due to the scarcity of domain-specific data and semantic distribution biases in domain-based contexts. |
| Approach: | They propose a framework that integrates an LLM-based knowledge enhancement workflow with a span-based Knowledge Fusion for Rich and Efficient Extraction model. |
| Outcome: | The proposed model achieves state-of-the-art performance on multiple domain-specific sequence labeling datasets and is highly efficient. |
Similar Papers
Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation (D19-1)
Copied to clipboard
| Challenge: | Existing domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual sample samples. |
| Approach: | They propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from a source domain model. |
| Outcome: | The proposed model outperforms baselines and state-of-the-art models on three sequence labeling tasks. |
More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
| Outcome: | The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders. |
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual sequence labeling require bilingual resources and require linguistic knowledge. |
| Approach: | They propose a multilingual language model with deep semantic Alignment to generate language-independent representations for cross-lingual sequence labeling. |
| Outcome: | The proposed model achieves state-of-the-art NER and POS performance across European languages and on distant language pairs such as English and Chinese. |
Enhanced Language Representation with Label Knowledge for Span Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to extract text spans from plain text do not fully exploit label knowledge. |
| Approach: | They propose a model to integrate label knowledge into text representations by encoding texts and annotations independently and then integrating label knowledge with an elaborate-designed semantics fusion module. |
| Outcome: | The proposed model achieves state-of-the-art performance on four benchmarks and reduces training time and inference time by 76% and 77% on average compared with the existing paradigm. |
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Named Entity Recognition, Relation Extraction, Semantic Role Labeling are examples of sequence labeling problems that require finetuning to the target format. |
| Approach: | They propose a dynamic sparse finetuning strategy that selectively focuses on a fraction of parameters, informed by feedback from highly regressing examples. |
| Outcome: | The proposed approach improves performance in low-resource settings and in extreme low-level settings. |
Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding (2026.findings-acl)
Copied to clipboard
| Challenge: | Negation is a common and important semantic feature in natural language, yet Large Language Models struggle when negation is involved in natural learning tasks. |
| Approach: | They propose to augment existing corpora with negation by automatically augmenting existing ones with negations by combining multiple triples with if-then relations. |
| Outcome: | The proposed approach yields two new corpora containing over 2M triples with if-then relations. |
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER). |
| Approach: | They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data. |
| Outcome: | The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities. |
Augmented Natural Language for Generative Sequence Labeling (2020.emnlp-main)
Copied to clipboard
| Challenge: | generative framework for joint sequence labeling and sentence-level classification is general purpose, performing well on few-shot learning, low resource, and high resource tasks. |
| Approach: | They propose a generative framework for joint sequence labeling and sentence-level classification . their framework incorporates label semantics and shares knowledge across tasks . |
| Outcome: | The proposed model performs on few-shot learning, slot labeling, and intent classification benchmarks. |
Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition (2024.acl-short)
Copied to clipboard
| Challenge: | specialized fields such as science and biology face significant challenges due to the scarcity of quality data. |
| Approach: | They propose a guidance data augmentation technique that abstracts context and sentence structure and maintains context-entity relationships for DA. |
| Outcome: | The proposed method enhances the training performance of named entity recognition tasks while maintaining context-entity relationships. |
Rethinking the Role of LLMs for Document-level Relation Extraction: a Refiner with Task Distribution and Probability Fusion (2025.naacl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) provides a broad context for extracting relations for entities. |
| Approach: | They propose a method that utilizes LLMs as a refiner and task distribution and probability fusion to refine LLM-based relation extraction methods. |
| Outcome: | The proposed method outperforms existing LLM-based methods without fine-tuning by 25.2% F1. |