Cross-lingual Semantic Role Labeling with Model Transfer
This work addresses cross-lingual SRL for NLP applications, but it is incremental as it builds on prior transfer methods with new feature combinations.
The paper tackles cross-lingual semantic role labeling by developing an end-to-end model that uses universal features and transfer methods, finding that gold syntax features are crucial and universal dependency structures provide the best performance improvements.
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.