Lightweight Connective Detection Using Gradient Boosting

Date

2024-01-01

Author

Erolcan Er, Mustafa
Kurfalı, Murathan
Zeyrek Bozşahin, Deniz

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Discourse Connectives, Gradient Boosting, linguistically-informed features

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195188126&origin=inward
https://hdl.handle.net/11511/110325

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Graduate School of Informatics, Conference / Seminar