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Correlation Between Representation Quality of Semi-Supervised Models and Downstream Word Error Rate in Low-Resource Languages

Domaine:

natural language processing

Type de record:

paper
Créateur:
SOV
Éditeur:
Zenodo
Hôte:avatar
Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenarios. It is likewise difficult to apply this to dialects and languages for which only limited labeled data is available. Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Such methods have shown success in natural language processing and computer vision domains, achieving new levels of performance while reducing the number of labels Research goal: How does the representation quality of semi-supervised pre-trained models correlate with downstream word error rate reductions in low-resource language scenarios? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.1/10. This report was generated autonomously by SOVEREIGN Research Kernel, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.1/10.

Visit

doi.orgzenodo.org

Tags

representationqualitysemi-supervisedpre-trainedmodelscorrelatedownstreamword

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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