Abstract
Understanding sentiment in complex textual expressions remains a fundamental challenge in affective computing. To address this, we propose a Dynamic Fusion Learning Model (DyFuLM), a multimodal framework designed to capture both hierarchical semantic representations and fine-grained emotional nuances. DyFuLM introduces two key moodules: a Hierarchical Dynamic Fusion module that adaptively integrates multi-level features, and a Gated Feature Aggregation module that regulates cross-layer information ffow to achieve balanced representation learning. Comprehensive experiments on multi-task sentiment datasets demonstrate that DyFuLM achieves 82.64% coarse-grained and 68.48% fine-grained accuracy, yielding the lowest regression errors (MAE = 0.0674, MSE = 0.0082) and the highest R^2 coefficient of determination (R^2= 0.6903). Furthermore, the ablation study validates the effectiveness of each module in DyFuLM. When all modules are removed, the accuracy drops by 0.91% for coarse-grained and 0.68% for fine-grained tasks. Keeping only the gated fusion module causes decreases of 0.75% and 0.55%, while removing the dynamic loss mechanism results in drops of 0.78% and 0.26% for coarse-grained and fine-grained sentiment classification, respectively. These results demonstrate that each module contributes significantly to feature interaction and task balance. Overall, the experimental findings further validate that DyFuLM enhances sentiment representation and overall performance through effective hierarchical feature fusion.
Abstract (translated)
理解复杂文本表达中的情感仍然是情感计算中的一个基本挑战。为了解决这一问题，我们提出了一种动态融合学习模型（DyFuLM），这是一种多模态框架，旨在捕捉层次化的语义表示和细微的情感差异。DyFuLM引入了两个关键模块：一种是层级动态融合模块，它能够自适应地整合多层次特征；另一种是有门控特性聚合的模块，该模块调节跨层信息流以实现平衡的表示学习。 在多任务情感数据集上的全面实验表明，DyFuLM实现了82.64%的粗粒度准确率和68.48%的细粒度准确率，并且获得了最低的回归误差（MAE = 0.0674，MSE = 0.0082）以及最高的R^2决定系数（R^2= 0.6903）。此外，消融实验验证了DyFuLM中每个模块的有效性。当移除所有模块时，粗粒度和细粒度任务的准确率分别下降了0.91% 和0.68%；仅保留门控融合模块导致准确率分别降低了0.75% 和0.55%，而去除动态损失机制则使粗粒度和细粒度情感分类的准确率分别下降了0.78% 和0.26%。这些结果表明，每个模块在特征交互和任务平衡中都发挥了重要作用。 总体而言，实验发现进一步验证了DyFuLM通过有效的层次化特征融合增强了情感表示和整体性能。
URL
https://arxiv.org/abs/2512.01410
