A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition

• URL: http://arxiv.org/abs/2302.13610v2
• Date: Wed, 12 Apr 2023 06:10:11 GMT
• Title: A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
• Authors: Guanting Dong and Zechen Wang and Liwen Wang and Daichi Guo and Dayuan Fu and Yuxiang Wu and Chen Zeng and Xuefeng Li and Tingfeng Hui and Keqing He and Xinyue Cui and Qixiang Gao and Weiran Xu
• Abstract summary: Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. We propose a Prototypical Semantic Decoupling method via joint Contrastive learning (PSDC) for few-shot NER. Experimental results on two few-shot NER benchmarks demonstrate that PSDC consistently outperforms the previous SOTA methods in terms of overall performance.
• Score: 24.916377682689955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing prototype-based sequence labeling models tend to memorize entity mentions which would be easily confused by close prototypes. In this paper, we proposed a Prototypical Semantic Decoupling method via joint Contrastive learning (PSDC) for few-shot NER. Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference. Besides, we further introduce joint contrastive learning objectives to better integrate two kinds of decoupling information and prevent semantic collapse. Experimental results on two few-shot NER benchmarks demonstrate that PSDC consistently outperforms the previous SOTA methods in terms of overall performance. Extensive analysis further validates the effectiveness and generalization of PSDC.

Related papers

• Negative Prototypes Guided Contrastive Learning for WSOD [8.102080369924911]
  Weakly Supervised Object Detection (WSOD) with only image-level annotation has recently attracted wide attention. We propose the Negative Prototypes Guided Contrastive learning architecture. Our proposed method achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-06-04T08:16:26Z)
• RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction [35.246592734300414]
  We propose a relation-aware prototype learning method for FSDLRE. Our method effectively refines the relation prototypes and generates task-specific NOTA prototypes.
  arXiv  Detail & Related papers  (2023-10-24T11:35:23Z)
• MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition [75.87566793111066]
  We propose a noise-robust prototype network named MProto for the DS-NER task. MProto represents each entity type with multiple prototypes to characterize the intra-class variance. To mitigate the noise from incomplete labeling, we propose a novel denoised optimal transport (DOT) algorithm.
  arXiv  Detail & Related papers  (2023-10-12T13:02:34Z)
• A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER [26.008350261239617]
  We propose a Multi-Task Semantic Decomposition Framework via Joint Task-specific Pre-training for few-shot NER. We introduce two novel pre-training tasks: Demonstration-based Masked Language Modeling (MLM) and Class Contrastive Discrimination. In the downstream main task, we introduce a multi-task joint optimization framework with the semantic decomposing method, which facilitates the model to integrate two different semantic information for entity classification.
  arXiv  Detail & Related papers  (2023-08-28T12:46:21Z)
• Prototype-based Embedding Network for Scene Graph Generation [105.97836135784794]
  Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. Due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category. Prototype-based Embedding Network (PE-Net) models entities/predicates with prototype-aligned compact and distinctive representations. PL is introduced to help PE-Net efficiently learn such entitypredicate matching, and Prototype Regularization (PR) is devised to relieve the ambiguous entity-predicate matching.
  arXiv  Detail & Related papers  (2023-03-13T13:30:59Z)
• SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition [45.012327072558975]
  Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. We propose a seminal span-based prototypical network (SpanProto) that tackles few-shot NER via a two-stage approach. In the span extraction stage, we transform the sequential tags into a global boundary matrix, enabling the model to focus on the explicit boundary information. For mention classification, we leverage prototypical learning to capture the semantic representations for each labeled span and make the model better adapt to novel-class entities.
  arXiv  Detail & Related papers  (2022-10-17T12:59:33Z)
• Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation [55.339405417090084]
  We propose a dual prototypical contrastive learning approach tailored to the few-shot semantic segmentation (FSS) task. The main idea is to encourage the prototypes more discriminative by increasing inter-class distance while reducing intra-class distance in prototype feature space. We demonstrate that the proposed dual contrastive learning approach outperforms state-of-the-art FSS methods on PASCAL-5i and COCO-20i datasets.
  arXiv  Detail & Related papers  (2021-11-09T08:14:50Z)
• Attentional Prototype Inference for Few-Shot Segmentation [128.45753577331422]
  We propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods.
  arXiv  Detail & Related papers  (2021-05-14T06:58:44Z)
• Prototypical Representation Learning for Relation Extraction [56.501332067073065]
  This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data. We learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art relational models.
  arXiv  Detail & Related papers  (2021-03-22T08:11:43Z)
• Part-aware Prototype Network for Few-shot Semantic Segmentation [50.581647306020095]
  We propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes.
  arXiv  Detail & Related papers  (2020-07-13T11:03:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.