@inproceedings{gordeev-etal-2025-hypercomplex,
    title = "Hypercomplex Transformer: Novel Attention Mechanism",
    author = "Gordeev, Maxim  and
      Aleksandr, Zuev  and
      Bakulin, Mikhail  and
      Latyshev, Andrey  and
      Kozlov, Dmitry  and
      Yao, Yiwu  and
      Anastasia, Voronova",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.115/",
    pages = "1845--1851",
    ISBN = "979-8-89176-303-6",
    abstract = "Self-attention mechanisms have become foundational across modern deep learning architectures. Recent efforts focus on improving their efficiency, particularly for signal processing tasks. The existing approaches employ complex-valued representations for inputs and weights and achieve higher accuracy at the cost of increased model size and inference latency. Dual-numbered algebra offers a promising alternative that allows efficient multiplication and faster inference with the same representational capacity. Inspired by previous studies in the field of hypercomplex neural networks, we introduce a generalized hypercomplex attention block and integrate it into Transformer-based models for EEG classification. Our experiments include adaptation of the hypercomplex models, so that the number of parameters is equal to that of their real-valued counterparts. Across all scenarios, the dual- and complex-numbered models consistently outperform the real ones, demonstrating superior accuracy. This work presents hypercomplex attention as a competitive and computationally efficient strategy with potential value to solve multiple NLP tasks."
}