CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation

• URL: http://arxiv.org/abs/2310.08944v1
• Date: Fri, 13 Oct 2023 08:19:31 GMT
• Title: CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation
• Authors: Carel van Niekerk, Christian Geishauser, Michael Heck, Shutong Feng, Hsien-chin Lin, Nurul Lubis, Benjamin Ruppik and Renato Vukovic and Milica Ga\v{s}i\'c
• Abstract summary: Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets. We present textbfCAMELL, a pool-based active learning framework tailored for sequential multi-output problems.
• Score: 6.918298428336528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets, a requirement that is particularly cumbersome for sequential tasks. The quality of annotations tends to deteriorate with the transition from expert-based to crowd-sourced labelling. To address these challenges, we present \textbf{CAMELL} (Confidence-based Acquisition Model for Efficient self-supervised active Learning with Label validation), a pool-based active learning framework tailored for sequential multi-output problems. CAMELL possesses three core features: (1) it requires expert annotators to label only a fraction of a chosen sequence, (2) it facilitates self-supervision for the remainder of the sequence, and (3) it employs a label validation mechanism to prevent erroneous labels from contaminating the dataset and harming model performance. We evaluate CAMELL on sequential tasks, with a special emphasis on dialogue belief tracking, a task plagued by the constraints of limited and noisy datasets. Our experiments demonstrate that CAMELL outperforms the baselines in terms of efficiency. Furthermore, the data corrections suggested by our method contribute to an overall improvement in the quality of the resulting datasets.

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