Cross-Lingual Transfer for Semantic Role Labeling in Indonesian

Abstract

Semantic role labeling is a semantic analysis task that aims to identify the semantic relationships within a sentence, such as who did what to whom, where, when, and so on. Current semantic role labeling (SRL) models for the Indonesian language still face challenges in achieving strong performance due to the limited availability of annotated corpora, especially compared with English SRL models. Therefore, this paper develops an Indonesian SRL model using cross-lingual transfer. This approach addresses the data scarcity problem in Indonesian SRL by leveraging the availability of annotated English-language corpora. The method uses multilingual models and SRL datasets from both English and Indonesian. The multilingual models used in this study are XLM-R and mT5, both in base and large configurations. The datasets include Universal PropBank Indonesia and Gojali’s dataset for Indonesian, and CoNLL-2012 for English. Evaluation was conducted using test data from Universal PropBank Indonesia and Gojali’s dataset. Among all developed models, XLM-R large with cross-lingual transfer achieved the best performance, with an F1 score of 0.916 on Gojali’s dataset and 0.858 on the combined Universal PropBank Indonesia and Gojali datasets.