Lightweight Connective Detection Using Gradient Boosting
This work addresses discourse relation annotation for scenarios with limited resources, offering a practical solution but is incremental as it builds on existing methods with efficiency improvements.
The paper tackles discourse connective detection by introducing a lightweight system using gradient boosting with simple features, achieving competitive results and significant time gains on CPU while maintaining stable performance across two languages.
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.