As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning

Vamvas, Jannis; Sennrich, Rico

doi:10.48550/arxiv.2203.01927

Cited by 1 publication

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“…These approaches have shown remarkable effectiveness in various areas, including vision denoising [46], [47], machine translation [48], [49] and molecular structure prediction [50] and social networks [51], [52]. Unlike existing work, our work aims to extend the scope of contrastive learning to crowdsourcing by sampling positive instance pairs and proposing a new loss function.…”

Section: B Contrastive Learningmentioning

confidence: 99%

Learning From Crowds With Contrastive Representation

Pedrycz

2023

IEEE Access

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Crowdsourcing provides a practical approach to obtaining annotated data for data-hungry deep models. Due to its simplicity and practicality, simultaneously learning the annotation correction mechanism and the target classifier is widely studied and applied. Existing work has improved performance from the annotator and annotation process modeling perspective. However, the instance representation, which most directly affects model training, has been neglected. In this work, we investigate contrastive representation to improve learning from crowds. Specifically, we first sample confident instances and positive pairs using the pre-trained representation and human annotations. Then, we extend the supervised contrastive loss to obtain a noise-tolerant version that supports continuous consistency between labels. After that, we leverage the learned representations to train the classifier and annotator parameters. The process is generally designed as an end-to-end framework, CrowdCons, compatible with existing crowdsourcing models. Our approach is evaluated on three real-world crowdsourcing datasets; LabelMe, CIFAR10-H and Music. The experimental results show that it can significantly improve prediction accuracy, and the case study demonstrates the robustness of the model regarding noisy annotations.

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Section: B Contrastive Learningmentioning

confidence: 99%

Learning From Crowds With Contrastive Representation

Pedrycz

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning

Cited by 1 publication

References 0 publications

Learning From Crowds With Contrastive Representation

Learning From Crowds With Contrastive Representation

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