Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach
Simone Conia, Roberto Navigli · 2020
Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling.Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between highresource (e.g., English) and low-resource (e.g., German) settings.To overcome this issue, we propose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages.Our approach outperforms the state of the art in all the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of training data is available.Our objective is not to reject approaches that rely on syntax, rather to set a strong and consistent language-independent baseline for future innovations in Semantic Role Labeling.We release our model code and checkpoints at https