Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision
Abstract
Making use of weak or noisy signals, like the output of heuristic
methods or user click through data for training deep neural networks
is increasing, in particular for the tasks where an adequate
amount of data with true labels is not available. In a semi-supervised
setting, we can use a large set of data with weak labels to pretrain a
neural network and fine tune the parameters with a small amount
of data with true labels. However, these two independent stages do
not leverage the full capacity of clean information from true labels
during pretraining.
In this paper, we propose a semi-supervised learning method
where we train two neural networks in a multi-task fashion: a target
network and a confidence network. The target network is optimized
to perform a given task and is trained using a large set of unlabeled
data that are weakly annotated. We propose to weight the gradient
updates to the target network using the scores provided by the
second confidence network, which is trained on a small amount of
supervised data. Thus we avoid that the weight updates computed
from noisy labels harm the quality of the target network model. We
evaluate our learning strategy on two different tasks: document
ranking and sentiment classification. The results demonstrate that
our approach not only enhances the performance compared to the
baselines but also speeds up the learning process from weak labels.
Research Areas
