文献类型：期刊文献

英文题名：MCPL: Multi-model co-guided progressive learning for multimodal aspect-based sentiment analysis

作者：Zhang, Jing[1];Qu, Jiaqi[1];Liu, Jiangpei[1];Wang, Zhe[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China

年份：2024

卷号：301

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20243316861530);WOS:【SCI-EXPANDED(收录号:WOS:001294023800001)】；

基金：This study was funded by the Natural Science Foundation of Shang-hai, China (22ZR1418400) . Research on image sentiment analysis and expression based on human vision and cognitive psychology.

语种：英文

外文关键词：Multimodal aspect-based sentiment analysis; Progressive learning; Semi-supervised learning

摘要：Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to combine image and text information to extract aspect terms from sentences and predict aspect-based sentiment polarities. Previous joint approaches have either viewed this task as collapsed label sequence annotation or modelled each subtask as a unified index sequence generation task, overlooking the explicit relation among its subtasks. This type of method cannot effectively integrate the strengths of task-specific models and adapt to difficult joint prediction tasks. To address this problem, we propose a Multi-model Co-guided Progressive Learning (MCPL) strategy that leverages task-specific models and the correlations among downstream tasks to expand high-quality training datasets, and provides progressive supervision signals to enhance the model's ability to adapt from simple to difficult tasks. Based on MCPL, we construct a Phase-wise Progressive Learning (PPL) module for MABSA, in which three Task Affinity-based Pseudo-label Generation (TAPG) modules are proposed for task-specific label expansion. The TAPG modules leverage the task correlations among three MABSA downstream tasks for co-guided teaching, and the PPL module utilises their gradual relationship from aspect term extraction to perform sentiment classification and then joint prediction for progressive learning. Experiments on widely used public datasets demonstrate that the proposed MCPL method achieves excellent performance on the three subtasks of MABSA and outperforms most state-of-the-art methods. The source code is publicly available at https://github.com/qujiaqi-babu/MCPL.