Learning with Noisy Labels for Sentence-level Sentiment Classification

TL;DR

This paper introduces NetAb, a dual-network deep learning model designed to improve sentence-level sentiment classification accuracy when training data contains noisy labels, by jointly modeling noisy and clean labels.

Contribution

The paper presents a novel dual-network CNN architecture, NetAb, that effectively handles noisy labels during training for sentiment analysis.

Findings

NetAb outperforms existing methods on noisy sentiment datasets.

The mutual reinforcement training improves label noise robustness.

Experimental results validate the effectiveness of the proposed approach.

Abstract

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.