Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection

EMNLP 2020

Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection

Dec 11, 2020
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"Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection" is work conducted by Jingfeng Yang, Zhaoran Ma, and Diyi Yang at the Machine Learning Center at Georgia Tech. This paper was accepted to the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). About ML@GT The Machine Learning Center was founded in 2016 as an interdisciplinary research center (IRC) at the Georgia Institute of Technology. Since then, we have grown to include over 190 affiliated faculty members and 60 Ph.D. students, all publishing at world-renowned conferences. The center aims to research and develop innovative and sustainable technologies using machine learning and artificial intelligence (AI) that serve our community in socially and ethically responsible ways. www.ml.gatech.edu

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