  Noisy labels in large E-commerce product data (i.e., product items are placed
into incorrect categories) are a critical issue for product categorization task
because they are unavoidable, non-trivial to remove and degrade prediction
performance significantly. Training a product title classification model which
is robust to noisy labels in the data is very important to make product
classification applications more practical. In this paper, we study the impact
of instance-dependent noise to performance of product title classification by
comparing our data denoising algorithm and different noise-resistance training
algorithms which were designed to prevent a classifier model from over-fitting
to noise. We develop a simple yet effective Deep Neural Network for product
title classification to use as a base classifier. Along with recent methods of
stimulating instance-dependent noise, we propose a novel noise stimulation
algorithm based on product title similarity. Our experiments cover multiple
datasets, various noise methods and different training solutions. Results
uncover the limit of classification task when noise rate is not negligible and
data distribution is highly skewed.
