Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus

• URL: http://arxiv.org/abs/2004.06295v2
• Date: Thu, 7 May 2020 03:43:42 GMT
• Title: Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
• Authors: Hao Fei and Meishan Zhang and Donghong Ji
• Abstract summary: Cross-lingual semantic role labeling is one promising way to address the problem. We propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective.
• Score: 41.031187560839555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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