Challenge: Named Entity Recognition (NER) tasks are a well-studied and fundamental task within Natural Language Understanding (NLU).
Approach: They propose to incorporate entity contrastive learning into a virtual assistant system to improve NER models by clustering similar inputs closer together in a learned representation space.
Outcome: The proposed model improves against a production baseline system that does not use contrastive learning.

Similar Papers

Noise-Robust Training with Dynamic Loss and Contrastive Learning for Distantly-Supervised Named Entity Recognition (2023.findings-acl)

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Challenge: Named entity recognition (NER) is a task in natural language processing that aims at locating entity mentions in a given sentence and assigning them to certain types.
Approach: They propose to use a dynamic loss function to better adapt to the changing noise during the training process and incorporate token level contrastive learning to fully utilize the noisy data.
Outcome: The proposed method outperforms existing NER models on three benchmark datasets and outperformed existing models by significant margins.
Large Margin Representation Learning for Robust Cross-lingual Named Entity Recognition (2025.acl-long)

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Challenge: Existing approaches to name entity recognition neglect distribution skewness and pseudo-label bias . despite promising results, current approaches neglect these problems .
Approach: They propose a framework that optimizes an adaptively reweighted contrastive loss to handle class skewness and pseudo-label bias.
Outcome: The proposed framework outperforms existing methods on multiple benchmarks.
When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World (2026.eacl-industry)

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Challenge: Large Language Models excel at understanding conversational semantics, but lack of data makes them impractical for production deployment.
Approach: They propose a pipeline for generating multilingual conversational NER datasets with minimal human validation and a framework that leverages LLMs as semantic filters combined with catalog-based entity grounding to label live traffic data.
Outcome: The proposed framework outperforms existing models on public and private conversations by 97.12% on CoNLL-2003 and 83.09% on OntoNotes 5.0.
Supporting Clustering with Contrastive Learning (2021.naacl-main)

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Challenge: Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space, but different categories overlap with each other at the beginning of the learning process.
Approach: They propose a framework to leverage contrastive learning to promote better separation between different categories by optimizing a clustering objective defined in the representation space.
Outcome: The proposed framework improves state-of-the-art accuracy and normalized mutual information on short text clustering and combines top-down and bottom-up instance discrimination to achieve better distances.
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)

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Challenge: Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance.
Approach: They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER)
Outcome: The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs.
Rethinking Negative Instances for Generative Named Entity Recognition (2024.findings-acl)

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Challenge: Named Entity Recognition (NER) models are constrained by a pre-defined label set and require extensive human annotations, which limits their flexibility and adaptability to unseen tasks.
Approach: They propose a Generative NER system that shows improved zero-shot performance across unseen entity domains by introducing contextual information and delineating label boundaries.
Outcome: The proposed model outperforms state-of-the-art methods in zero-shot evaluation.
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)

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Challenge: Existing pre-training objectives do not explicitly model relational facts in text . Experimental results show that ERICA can improve typical PLMs on several language understanding tasks, including relation extraction, entity typing and question answering.
Approach: They propose a contrastive learning framework ERICA to obtain a deep understanding of entities and relations in text.
Outcome: The proposed framework can improve PLMs on several language understanding tasks, especially under low-resource settings.
Learning “O” Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NER (2023.acl-long)

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Challenge: Existing Named Entity Recognition systems are typically trained on a large-scale dataset with predefined entity classes, then deployed for entity recognition on the test data without further adaptation or refinement.
Approach: They propose a representation learning method that adaptively detects entity clusters in "O" and two effective distance-based relabeling strategies for better learning the old classes.
Outcome: The proposed method achieves 10.62% improvement over the baseline methods.
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.
Outcome: The proposed framework outperforms state-of-the-art models on five datasets on named entity recognition (NER) tasks.
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition (2022.findings-naacl)

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Challenge: Existing methods to locate and classify entities using knowledge bases and unlabeled corpus are expensive and limited application.
Approach: They propose to use a method to directly learn the distant label refinement knowledge by imitating annotations of different qualities and comparing them in contrastive learning frameworks.
Outcome: The proposed method can give modified suggestions on distant data without additional supervised labels and thus reduces the requirement on the quality of the knowledge bases.

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