Lightweight Connective Detection Using Gradient Boosting

Autor:	Er, Mustafa Erolcan, Kurfalı, Murathan, Zeyrek, Deniz
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language I.2.7
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 2 figures, 5 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.13793 Zobrazit plný text záznamu View this record from Arxiv