Data Uncertainty-Aware Learning for Multimodal Aspect-based Sentiment Analysis
Journal:
arXiv
Published Date:
Dec 2, 2024
Abstract
As a fine-grained task, multimodal aspect-based sentiment analysis (MABSA)
mainly focuses on identifying aspect-level sentiment information in the
text-image pair. However, we observe that it is difficult to recognize the
sentiment of aspects in low-quality samples, such as those with low-resolution
images that tend to contain noise. And in the real world, the quality of data
usually varies for different samples, such noise is called data uncertainty.
But previous works for the MABSA task treat different quality samples with the
same importance and ignored the influence of data uncertainty. In this paper,
we propose a novel data uncertainty-aware multimodal aspect-based sentiment
analysis approach, UA-MABSA, which weighted the loss of different samples by
the data quality and difficulty. UA-MABSA adopts a novel quality assessment
strategy that takes into account both the image quality and the aspect-based
cross-modal relevance, thus enabling the model to pay more attention to
high-quality and challenging samples. Extensive experiments show that our
method achieves state-of-the-art (SOTA) performance on the Twitter-2015
dataset. Further analysis demonstrates the effectiveness of the quality
assessment strategy.
