M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal
  Aspect-based Sentiment Analysis
Fei Zhao Chunhui Li Zhen Wu Yawen Ouyang Jianbing Zhang Xinyu Dai
Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained
Sentiment Analysis task, which has attracted growing research interests
recently. Existing work mainly utilizes image information to improve the
performance of MABSA task. However, most of the studies overestimate the
importance of images since there are many noise images unrelated to the text in
the dataset, which will have a negative impact on model learning. Although some
work attempts to filter low-quality noise images by setting thresholds, relying
on thresholds will inevitably filter out a lot of useful image information.
Therefore, in this work, we focus on whether the negative impact of noisy
images can be reduced without modifying the data. To achieve this goal, we
borrow the idea of Curriculum Learning and propose a Multi-grained
Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by
adjusting the order of training data. Extensive experimental results show that
our framework consistently outperforms state-of-the-art work on three sub-tasks
of MABSA.