  Weakly supervised learning aims at coping with scarce labeled data. Previous
weakly supervised studies typically assume that there is only one kind of weak
supervision in data. In many applications, however, raw data usually contains
more than one kind of weak supervision at the same time. For example, in user
experience enhancement from Didi, one of the largest online ride-sharing
platforms, the ride comment data contains severe label noise (due to the
subjective factors of passengers) and severe label distribution bias (due to
the sampling bias). We call such a problem as "compound weakly supervised
learning". In this paper, we propose the CWSL method to address this problem
based on Didi ride-sharing comment data. Specifically, an instance reweighting
strategy is employed to cope with severe label noise in comment data, where the
weights for harmful noisy instances are small. Robust criteria like AUC rather
than accuracy and the validation performance are optimized for the correction
of biased data label. Alternating optimization and stochastic gradient methods
accelerate the optimization on large-scale data. Experiments on Didi
ride-sharing comment data clearly validate the effectiveness. We hope this work
may shed some light on applying weakly supervised learning to complex real
situations.
