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Contextual Data Augmentation for TaskOriented Dialog Systems Dustin Axman, Avik Ray, Shubham Garg, and Jing Huang Amazon Alexa dax, avikray, gargshu@amazon.com, jinghuang.zhu@gmail.comAbstract. Collection
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Contextual data augmentation is used to enhance the quality and quantity of data by sampling additional contextual information, which helps improve model performance in machine learning applications.
Organizations and researchers who are involved in data-intensive projects and need to report their data augmentation practices, particularly in compliance with regulatory frameworks, are required to file contextual data augmentation.
To fill out contextual data augmentation, individuals need to provide details about the augmented data, describe the contextual parameters used in the augmentation process, and submit relevant documentation that supports their methodology.
The purpose of contextual data augmentation is to improve the robustness and validity of data, ensuring that machine learning models are trained on a diverse range of scenarios, which ultimately leads to better generalization in real-world applications.
Information that must be reported includes details on the original dataset, the augmentation techniques applied, the contextual factors considered, and any performance metrics that demonstrate the effectiveness of the augmentation.
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