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Meta-Based Self-Training and Re-Weighting for Aspect-Based Sentiment Analysis

过度拟合 计算机科学 人工智能 机器学习 情绪分析 任务(项目管理) 人工神经网络 加权 医学 管理 经济 放射科
作者
Kai He,Rui Mao,Tieliang Gong,Chen Li,Erik Cambria
出处
期刊:IEEE Transactions on Affective Computing [Institute of Electrical and Electronics Engineers]
卷期号:14 (3): 1731-1742 被引量:54
标识
DOI:10.1109/taffc.2022.3202831
摘要

Aspect-based sentiment analysis (ABSA) means to identify fine-grained aspects, opinions, and sentiment polarities. Recent ABSA research focuses on utilizing multi-task learning (MTL) to achieve less computational costs and better performance. However, there are certain limits in MTL-based ABSA. For example, unbalanced labels and sub-task learning difficulties may result in the biases that some labels and sub-tasks are overfitting, while the others are underfitting. To address these issues, inspired by neuro-symbolic learning systems, we propose a meta-based self-training method with a meta-weighter (MSM). We believe that a generalizable model can be achieved by appropriate symbolic representation selection (in-domain knowledge) and effective learning control (regulation) in a neural system. Thus, MSM trains a teacher model to generate in-domain knowledge (e.g., unlabeled data selection and pseudo-label generation), where the generated pseudo-labels are used by a student model for supervised learning. Then, the meta-weighter of MSM is jointly trained with the student model to provide each instance with sub-task-specific weights to coordinate their convergence rates, balancing class labels, and alleviating noise impacts introduced from self-training. The following experiments indicate that MSM can utilize 50% labeled data to achieve comparable results to state-of-arts models in ABSA and outperform them with all labeled data.
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