  Large datasets in NLP suffer from noisy labels, due to erroneous automatic
and human annotation procedures. We study the problem of text classification
with label noise, and aim to capture this noise through an auxiliary noise
model over the classifier. We first assign a probability score to each training
sample of having a noisy label, through a beta mixture model fitted on the
losses at an early epoch of training. Then, we use this score to selectively
guide the learning of the noise model and classifier. Our empirical evaluation
on two text classification tasks shows that our approach can improve over the
baseline accuracy, and prevent over-fitting to the noise.
