ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation (2023.emnlp-main)
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| Challenge: | Recent studies indicate that learning from individual annotations outperforms learning from aggregated labels, though they require a considerable amount of annotation. |
| Approach: | They propose to use a multi-head model to learn from disagreements in an active learning setting to identify annotators with a single head. |
| Outcome: | The proposed model outperforms a single-head model in terms of uncertainty estimation and prediction while saving 70% of the annotation budget. |
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| Challenge: | Disagreement in annotations is natural for humans, depending on background, identity, positionality . many active learning approaches focus on examples where model entropy and annotator entropicy are the most different. |
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Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations (2022.tacl-1)
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| Challenge: | Annotators may systematically disagree with one another, reflecting their individual biases and values, especially in the case of subjective tasks such as detecting affect, aggression, and hate speech. |
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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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When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks (2023.emnlp-main)
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| Challenge: | a number of studies have questioned assumptions of majority vote aggregated labels. |
| Approach: | They construct a model that predicts individual annotator ratings on potentially offensive text and combines this information with the predicted target group of the text to predict the ratings of target group members. |
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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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Annotator-Centric Active Learning for Subjective NLP Tasks (2024.emnlp-main)
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| Challenge: | Annotator-centric active learning addresses the high costs of collecting human annotations by strategically annotating the most informative samples. |
| Approach: | They propose annotator-centric active learning which incorporates an annotation strategy following data sampling to approximate the full diversity of human judgments. |
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Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning (2023.acl-long)
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| Challenge: | Annotator disagreements are resolved before learning takes place, but researchers question the performance of a system when annotators disagree. |
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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. |
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| Outcome: | The proposed method significantly improves performance on NLP tasks beyond the standard approach and prior work. |
Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown remarkable capabilities to understand and generate human languages, supporting applications such as question answering, coding, and psychological counseling. |
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You Are What You Annotate: Towards Better Models through Annotator Representations (2023.findings-emnlp)
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| Challenge: | Annotator disagreement is ubiquitous in natural language processing tasks. |
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| Outcome: | The proposed model improves on an existing dataset with eight annotators with inherent disagreements while increasing model size by 1%. |