Lightweight Connective Detection Using Gradient Boosting

TL;DR

This paper presents a lightweight, gradient boosting-based system for detecting discourse connectives that is efficient, robust across languages, and suitable for resource-limited scenarios, achieving competitive results without deep neural networks.

Contribution

The authors introduce a simple, low-complexity discourse connective detection method using gradient boosting, reducing computational costs while maintaining performance.

Findings

Achieves competitive accuracy with low computational complexity

Performs consistently across two unrelated languages

Offers significant time savings on CPU-based systems

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.