| Challenge: | Xu et al., 2015) proposed a noise reduction mechanism to disentangle semantics of words . hard and soft attention mechanisms are used to reduce noise in NLP tasks . |
| Approach: | They propose a prism module to disentangle semantic aspects of words and reduce noise . they propose combining prism modules with downstream models to improve model performance . |
| Outcome: | The proposed method significantly improves the performance of baselines on named entity recognition (NER) tasks. |
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| Challenge: | Existing studies have shown that external knowledge is important for Named Entity Recognition . |
| Approach: | They propose a modular framework that divides knowledge into four categories according to depth . they show the effects when incrementally adding deeper knowledge . |
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Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)
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| Challenge: | Named entities pose a unique challenge to traditional methods of language modeling. |
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Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
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| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
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Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy (2025.coling-main)
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| Challenge: | Existing models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), which hinders the achievement of satisfactory performance. |
| Approach: | They propose a framework which fully leverages sentence-level information to improve OOE-NER performance by exploiting pre-trained language models' ability to understand target entity’s sentence context with a template set and refines sentence representation based on positive and negative templates. |
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Robust to Noise Models in Natural Language Processing Tasks (P19-2)
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| Challenge: | Existing spelling correction systems are far from perfect for noise-sensitive texts . a new way to handle noise is to make models robust to noise. |
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Disentangling Meaning and Language Components in Diverse Multilingual Sentence Embeddings (2026.acl-srw)
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| Challenge: | Existing studies have reported language specificity in multilingual sentence embeddings, resulting in language-specific subspaces. |
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Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange (D19-1)
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| Challenge: | Existing methods to adjust semantics of text while preserving its style have not been investigated to the best of our knowledge. |
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ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)
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Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie Zhou
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Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)
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| 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. |
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Pooled Contextualized Embeddings for Named Entity Recognition (N19-1)
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| Challenge: | Contextual string embeddings are a recent type of word embeddable that are useful for sequence labeling tasks. |
| Approach: | They propose a method that dynamically aggregates contextualized embeddings of each unique string . they then use a pooling operation to distill a ”global” word representation from all contextualized instances . |
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