DualKanbaFormer: Kolmogorov-Arnold Networks and State Space Model Transformer for Multimodal Aspect-based Sentiment Analysis
<|reference_start|>DualKanbaFormer: Kolmogorov-Arnold Networks and State Space Model Transformer for Multimodal Aspect-based Sentiment Analysis: Multimodal aspect-based sentiment analysis (MABSA) enhances sentiment detection by combining text with other data types like images. However, despite setting significant benchmarks, attention mechanisms exhibit limitations in efficiently modelling long-range dependencies between aspect and opinion targets within the text. They also face challenges in capturing global-context dependencies for visual representations. To this end, we propose Kolmogorov-Arnold Networks (KANs) and Selective State Space model (Mamba) transformer (DualKanbaFormer), a novel architecture to address the above issues. We leverage the power of Mamba to capture global context dependencies, Multi-head Attention (MHA) to capture local context dependencies, and KANs to capture non-linear modelling patterns for both textual representations (textual KanbaFormer) and visual representations (visual KanbaFormer). Furthermore, we fuse the textual KanbaFormer and visual KanbaFomer with a gated fusion layer to capture the inter-modality dynamics. According to extensive experimental results, our model outperforms some state-of-the-art (SOTA) studies on two public datasets.<|reference_end|>
@article{lawan2024dualkanbaformer:,
    title={DualKanbaFormer: Kolmogorov-Arnold Networks and State Space Model
  Transformer for Multimodal Aspect-based Sentiment Analysis},
    author={Adamu Lawan, Juhua Pu, Haruna Yunusa, Muhammad Lawan, Aliyu Umar,
  Adamu Sani Yahya},
    journal={arXiv preprint arXiv:2408.15379},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15379},
    primaryClass={cs.CL}
}