Multi-annotator Deep Learning: A Probabilistic Framework for Classification

• URL: http://arxiv.org/abs/2304.02539v2
• Date: Mon, 23 Oct 2023 23:08:02 GMT
• Title: Multi-annotator Deep Learning: A Probabilistic Framework for Classification
• Authors: Marek Herde, Denis Huseljic, Bernhard Sick
• Abstract summary: Training standard deep neural networks leads to subpar performances in multi-annotator supervised learning settings. We address this issue by presenting a probabilistic training framework named multi-annotator deep learning (MaDL) A modular network architecture enables us to make varying assumptions regarding annotators' performances. Our findings show MaDL's state-of-the-art performance and robustness against many correlated, spamming annotators.
• Score: 2.445702550853822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Solving complex classification tasks using deep neural networks typically requires large amounts of annotated data. However, corresponding class labels are noisy when provided by error-prone annotators, e.g., crowdworkers. Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings. We address this issue by presenting a probabilistic training framework named multi-annotator deep learning (MaDL). A downstream ground truth and an annotator performance model are jointly trained in an end-to-end learning approach. The ground truth model learns to predict instances' true class labels, while the annotator performance model infers probabilistic estimates of annotators' performances. A modular network architecture enables us to make varying assumptions regarding annotators' performances, e.g., an optional class or instance dependency. Further, we learn annotator embeddings to estimate annotators' densities within a latent space as proxies of their potentially correlated annotations. Together with a weighted loss function, we improve the learning from correlated annotation patterns. In a comprehensive evaluation, we examine three research questions about multi-annotator supervised learning. Our findings show MaDL's state-of-the-art performance and robustness against many correlated, spamming annotators.

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