| Challenge: | Existing methods to train deep neural networks with label noise are limited to image classification models . label noise is important because of the large number of errors and errors in training datasets . |
| Approach: | They propose a non-linear processing layer that models label noise into a convolutional neural network (CNN) they add a noise model layer on top of their target model to account for label noise . |
| Outcome: | The proposed approach is robust to label noise and can learn better sentences . it is based on extensive experiments on text classification datasets . |
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| Challenge: | Existing research on learning with noisy labels dates back to the 1980s, but it is still vibrant today. |
| Approach: | They propose a novel DNN model called NetAb to deal with noisy labels during training and train the networks using their respective loss functions in mutual reinforcement. |
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Noisy Label Regularisation for Textual Regression (2022.coling-1)
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| Challenge: | Existing methods to regularise noisy labels are ineffective in the face of noisy data. |
| Approach: | They propose a method that regularises noisy labels and prevents error propagation from the input layer. |
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Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling (N19-3)
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| Challenge: | In low-resource environments, self-training is less effective due to unreliable annotations . we combine self-teaching with noise handling to clean the self-labeled data . |
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NAT: Noise-Aware Training for Robust Neural Sequence Labeling (2020.acl-main)
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| Challenge: | Sequence labeling systems should perform reliably under ideal conditions and with corrupted inputs. |
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Adaptive Textual Label Noise Learning based on Pre-trained Models (2023.findings-emnlp)
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| Challenge: | Existing approaches to learning with noisy labels are limited due to the time and labor costs involved. |
| Approach: | They propose an adaptive warm-up and hybrid training frameworks to learn with noisy labels based on pre-trained models. |
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| Challenge: | Recent studies show that deep neural networks can memorize noisy labels with limited training time. |
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Model-agnostic Methods for Text Classification with Inherent Noise (2020.coling-industry)
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| Challenge: | Text classification is a fundamental problem in natural language processing, but its performance relies on high-quality annotations. |
| Approach: | They propose to use model-agnostic methods to handle inherent noise in large scale text classification that can be easily incorporated into existing machine learning workflows with minimal interruption. |
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Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)
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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang
| Challenge: | Existing approaches to reduce label noise rely on heuristics and sample losses. |
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Noise Learning for Text Classification: A Benchmark (2022.coling-1)
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| Challenge: | Existing noise learning methods for text classification are underdeveloped . authors propose a noise learning benchmark for text classification . |
| Approach: | They propose to use four state-of-the-art methods of noise learning from the image domain to classify text. |
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NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing (2023.findings-acl)
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| Challenge: | Large-scale datasets in the real world often contain label noise, which can cause model overfitting and degrade generalization. |
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