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.

本文研究了在电商商品大数据中存在的噪声标签及其对产品分类模型的影响，提出了一种基于实例依赖噪声的训练解决方案，在多个数据集及噪声方法上进行了实验，结果表明当噪声率不能忽略且数据分布高度倾斜时，分类任务的局限性。