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        <title>ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation</title>
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    <abstract>Label aggregation such as majority voting is commonly used to resolve annotator disagreement in dataset creation. However, this may disregard minority values and opinions. Recent studies indicate that learning from individual annotations outperforms learning from aggregated labels, though they require a considerable amount of annotation. Active learning, as an annotation cost-saving strategy, has not been fully explored in the context of learning from disagreement. We show that in the active learning setting, a multi-head model performs significantly better than a single-head model in terms of uncertainty estimation. By designing and evaluating acquisition functions with annotator-specific heads on two datasets, we show that group-level entropy works generally well on both datasets. Importantly, it achieves performance in terms of both prediction and uncertainty estimation comparable to full-scale training from disagreement, while saving 70% of the annotation budget.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.126</identifier>
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%0 Conference Proceedings
%T ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation
%A Wang, Xinpeng
%A Plank, Barbara
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-plank-2023-actor
%X Label aggregation such as majority voting is commonly used to resolve annotator disagreement in dataset creation. However, this may disregard minority values and opinions. Recent studies indicate that learning from individual annotations outperforms learning from aggregated labels, though they require a considerable amount of annotation. Active learning, as an annotation cost-saving strategy, has not been fully explored in the context of learning from disagreement. We show that in the active learning setting, a multi-head model performs significantly better than a single-head model in terms of uncertainty estimation. By designing and evaluating acquisition functions with annotator-specific heads on two datasets, we show that group-level entropy works generally well on both datasets. Importantly, it achieves performance in terms of both prediction and uncertainty estimation comparable to full-scale training from disagreement, while saving 70% of the annotation budget.
%R 10.18653/v1/2023.emnlp-main.126
%U https://aclanthology.org/2023.emnlp-main.126/
%U https://doi.org/10.18653/v1/2023.emnlp-main.126
%P 2046-2052

Markdown (Informal)

[ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation](https://aclanthology.org/2023.emnlp-main.126/) (Wang & Plank, EMNLP 2023)

• ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation (Wang & Plank, EMNLP 2023)

ACL

• Xinpeng Wang and Barbara Plank. 2023. ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2046–2052, Singapore. Association for Computational Linguistics.