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Conversational analysis systems are trained using noisy human labels andoften require heavy preprocessing during multi-modal feature extraction. Usingnoisy labels in single-task learning increases the risk of over-fitting.Auxiliary tasks could improve the performance of the primary task learningduring the same training -- this approach sits in the intersection of transferlearning and multi-task learning (MTL). In this paper, we explore how thepreprocessed data used for feature engineering can be re-used as auxiliarytasks, thereby promoting the productive use of data. Our main contributionsare: (1) the identification of sixteen beneficially auxiliary tasks, (2)studying the method of distributing learning capacity between the primary andauxiliary tasks, and (3) studying the relative supervision hierarchy betweenthe primary and auxiliary tasks. Extensive experiments on IEMOCAP and SEMAINEdata validate the improvements over single-task approaches, and suggest that itmay generalize across multiple primary tasks.

Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors