Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks (N19-1)
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| Challenge: | Existing models that estimate annotators' reliability only consider binary labels and multi-class labels. |
| Approach: | They propose an unsupervised model which can handle binary and multi-class labels and integrate neural networks to model the dependency between latent variables and instances. |
| Outcome: | The proposed model can handle binary and multi-class labels and can estimate reliability of annotators across instances. |
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| Challenge: | Obtaining annotations from experts is ideal, but this expertise is logistically and financially costly. |
| Approach: | They propose an annotation framework that supports the whole annotation pipeline from understanding the resources required for an annotation task to compiling the annotated dataset. |
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Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning (2021.naacl-main)
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| Challenge: | Prior work shows that disagreement between annotators can be useful in training models. |
| Approach: | They propose to use disagreements as an auxiliary task in a multi-task neural network to incorporate disagreements into models. |
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Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance (2023.findings-emnlp)
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| Challenge: | Existing studies have shown that multi-annotator datasets can improve performance when they expand from a single annotation per instance to multiple annotations. |
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Instance-adaptive training with noise-robust losses against noisy labels (2021.emnlp-main)
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Learning with Different Amounts of Annotation: From Zero to Many Labels (2021.emnlp-main)
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| Challenge: | a lack of annotator agreement can hinder training of NLP systems . we propose a learning algorithm that can learn from training examples with zero, one, or multiple labels. |
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Denoising Multi-Source Weak Supervision for Neural Text Classification (2020.findings-emnlp)
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| Challenge: | Recent years have witnessed the rapid development of deep neural networks (DNNs) for text classification problems. |
| Approach: | They propose a label denoiser which estimates the source reliability using a conditional soft attention mechanism and reduces label noise by aggregating rule-annotated weak labels. |
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Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction (N19-1)
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| Challenge: | Modern NLP systems require high-quality annotations, but experts are expensive and lay annotators may not have the knowledge to provide high- quality annotations. |
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Don’t waste a single annotation: improving single-label classifiers through soft labels (2023.findings-emnlp)
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| Challenge: | Existing methods for annotating data are limited by ambiguity and lack of context in data samples. |
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Corpus Considerations for Annotator Modeling and Scaling (2024.naacl-long)
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| Challenge: | Recent trends in natural language processing and annotation tasks emphasize individual perspectives . annotator models that rely on a single ground truth may disregard valuable minority perspectives omissions . |
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Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo (2023.findings-acl)
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Tharindu Cyril Weerasooriya, Alexander Ororbia, Raj Bhensadadia, Ashiqur KhudaBukhsh, Christopher Homan
| Challenge: | a recent study shows that annotator disagreement is common in supervised learning . a simple neural model that learns to predict annotators' labels is competitive with other models that do not model specific annotations. |
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