Prior studies show that cross-lingual semantic role labeling (SRL) can be
achieved by model transfer under the help of universal features. In this paper,
we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that
incorporates a variety of universal features and transfer methods. We study
both the bilingual transfer and multi-source transfer, under gold or
machine-generated syntactic inputs, pre-trained high-order abstract features,
and contextualized multilingual word representations. Experimental results on
the Universal Proposition Bank corpus indicate that performances of the
cross-lingual SRL can vary by leveraging different cross-lingual features. In
addition, whether the features are gold-standard also has an impact on
performances. Precisely, we find that gold syntax features are much more
crucial for cross-lingual SRL, compared with the automatically-generated ones.
Moreover, universal dependency structure features are able to give the best
help, and both pre-trained high-order features and contextualized word
representations can further bring significant improvements.

本文探讨了如何通过使用多种跨语言特征和转移方法，实现端到端语义角色标注（SRL）模型的构建，并概括了实验结果，说明使用不同的跨语言特征和黄金特征可以显著提高跨语言 SRL 的性能。