Lightweight Connective Detection Using Gradient Boosting
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910417449648128 |
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| author | Er, Mustafa Erolcan Kurfalı, Murathan Zeyrek, Deniz |
| author_facet | Er, Mustafa Erolcan Kurfalı, Murathan Zeyrek, Deniz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13793 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lightweight Connective Detection Using Gradient Boosting Er, Mustafa Erolcan Kurfalı, Murathan Zeyrek, Deniz Computation and Language I.2.7 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. |
| title | Lightweight Connective Detection Using Gradient Boosting |
| topic | Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2404.13793 |