探索预训练变形器和双语迁移学习在阿拉伯语共同参考解决中的应用

Bonan Min
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引用次数: 3

摘要

在本文中,我们开发了双语迁移学习方法,通过双语或多语言预训练转换器利用额外的英语注释来提高阿拉伯语共指分辨率。我们发现双语迁移学习将基于强变换的神经共参考模型提高了2-4个F1。我们还系统地研究了几种预训练的变压器模型的有效性,这些模型在训练语料库、涵盖的语言和模型容量方面有所不同。我们最好的模型在阿拉伯语OntoNotes数据集上实现了64.55 F1的最新性能。我们的代码可以在https://github.com/bnmin/arabic_coref上公开获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution
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 state-of-the-art performance of 64.55 F1 on the Arabic OntoNotes dataset. Our code is publicly available at https://github.com/bnmin/arabic_coref.
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