@inproceedings{er-etal-2024-lightweight,
    title = "Lightweight Connective Detection Using Gradient Boosting",
    author = "Er, Mustafa Erolcan  and
      Kurfal{\i}, Murathan  and
      Zeyrek, Deniz",
    editor = "Bunt, Harry  and
      Ide, Nancy  and
      Lee, Kiyong  and
      Petukhova, Volha  and
      Pustejovsky, James  and
      Romary, Laurent",
    booktitle = "Proceedings of the 20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.isa-1.7/",
    pages = "53--59",
    abstract = "In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed approach sidesteps the computational demands of the current approaches that rely on deep neural networks. Considering its simplicity, our approach achieves competitive results while offering significant gains in terms of time even on CPU. Furthermore, the stable performance across two unrelated languages suggests the robustness of our system in the multilingual scenario. The model is designed to support the annotation of discourse relations, particularly in scenarios with limited resources, while minimizing performance loss."
}