Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach

Abstract

This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid data sampling method that combines SMOTE, a meta-weigher with a meta-based self-training method (MMS), and one-sided selection (OSS) to balance the distribution of classes. The method also utilizes condensed nearest neighbors (CNN) to remove noisy majority examples and redundant examples. The proposed technique is twofold, involving the creation of artificial instances using SMOTE-OSS-CNN to oversample the under-represented class distribution and the use of MMS to train an instructor model that produces in-field knowledge for pseudo-labeled examples. The student model uses these pseudo-labels for supervised learning, and the student model and MMS meta-weigher are jointly trained to give each example subtask-specific weights to balance class labels and mitigate the noise effects caused by self-training. The proposed technique is evaluated on a discharge summary dataset against six state-of-the-art approaches, and the results demonstrate that it outperforms these approaches with complete labeled data and achieves results equivalent to state-of-the-art methods that require all labeled data using aspect-based sentiment analysis (ABSA).