Lightweight Connective Detection Using Gradient Boosting

Erolcan Er M., Kurfalı M., ZEYREK BOZŞAHİN D.

20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation, ISA 2024, Torino, İtalya, 20 Mayıs 2024, ss.53-59 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Torino
  • Basıldığı Ülke: İtalya
  • Sayfa Sayıları: ss.53-59
  • Anahtar Kelimeler: Discourse Connectives, Gradient Boosting, linguistically-informed features
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.