  Deep neural networks (DNNs) can fit (or even over-fit) the training data very
well. If a DNN model is trained using data with noisy labels and tested on data
with clean labels, the model may perform poorly. This paper studies the problem
of learning with noisy labels for sentence-level sentiment classification. We
propose a novel DNN model called NetAb (as shorthand for convolutional neural
Networks with Ab-networks) to handle noisy labels during training. NetAb
consists of two convolutional neural networks, one with a noise transition
layer for dealing with the input noisy labels and the other for predicting
'clean' labels. We train the two networks using their respective loss functions
in a mutual reinforcement manner. Experimental results demonstrate the
effectiveness of the proposed model.
