When does deep multi-task learning work for loosely related document classification tasks?
Emma Kerinec, Chloé Braud, Anders Søgaard · 2018
This work aims to contribute to our understanding of when multi-task learning through parameter sharing in deep neural networks leads to improvements over single-task learning.We focus on the setting of learning from loosely related tasks, for which no theoretical guarantees exist.We therefore approach the question empirically, studying which properties of datasets and single-task learning characteristics correlate with improvements from multi-task learning.We are the first to study this in a text classification setting and across more than 500 different task pairs.