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Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors
[article]
2021
arXiv
pre-print
Conversational analysis systems are trained using noisy human labels and often require heavy preprocessing during multi-modal feature extraction. Using noisy labels in single-task learning increases the risk of over-fitting. Auxiliary tasks could improve the performance of the primary task learning during the same training -- this approach sits in the intersection of transfer learning and multi-task learning (MTL). In this paper, we explore how the preprocessed data used for feature engineering
arXiv:2112.03032v1
fatcat:rxcey4hz2nacxoka4fmhhfckxq