@inproceedings{chen-2025-jinan,
    title = "Jinan Smart Education at {BEA} 2025 Shared Task: Dual Encoder Architecture for Tutor Identification via Semantic Understanding of Pedagogical Conversations",
    author = "Chen, Lei",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.bea-1.78/",
    pages = "1034--1039",
    ISBN = "979-8-89176-270-1",
    abstract = "With the rapid development of smart education, educational conversation systems have become an important means to support personalized learning. Identifying tutors and understanding their unique teaching style are crucial to optimizing teaching quality. However, accurately identifying tutors from multi-round educational conversation faces great challenges due to complex contextual semantics, long-term dependencies, and implicit pragmatic relationships. This paper proposes a dual-tower encoding architecture to model the conversation history and tutor responses separately, and enhances semantic fusion through four feature interaction mechanisms. To further improve the robustness, this paper adopts a model ensemble voting strategy based on five-fold cross-validation. Experiments on the BEA 2025 shared task dataset show that our method achieves 89.65{\%} Marco-F1 in tutor identification, ranks fourth among all teams(4/20), demonstrating its effectiveness and potential in educational AI applications.We have made the corresponding code publicly accessible at \url{https://github.com/leibnizchen/Dual-Encoder}."
}