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.

一种轻量级语篇连接词检测系统，使用基于梯度提升训练的简单、低复杂度特征，避免了依赖深度神经网络的计算需求；考虑到其简洁性，该方法在 CPU 上即可取得竞争性结果，同时在两种不相关的语言之间表现稳定，这表明了该系统在多语言场景中的鲁棒性；此模型旨在支持语篇关系的标注，特别适用于资源有限的场景，同时尽量减少性能损失。