Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution
Bonan Min · 2021
In this paper, we develop bilingual transfer learning approaches to improve Arabic coreference resolution by leveraging additional English annotation via bilingual or multilingual pre-trained transformers.We show that bilingual transfer learning improves the strong transformer-based neural coreference models by 2-4 F1.We also systemically investigate the effectiveness of several pre-trained transformer models that differ in training corpora, languages covered, and model capacity.Our best model achieves a new stateof-the-art performance of 64.55 F1 on the Arabic OntoNotes dataset.Our code is publicly available at https://github.com/ bnmin/arabic_coref.