Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Hao Fei, Meishan Zhang, Donghong Ji · 2020
Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding.Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English.While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances.Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection.In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations.Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.