On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling

Although recent developments in neural architectures and pre-trained representations have greatly increased state-of-the-art model performance on fully-supervised semantic role labeling (SRL), the task remains challenging for languages where supervised SRL training data are not abundant. Cross-lingual learning can improve performance in this setting by transferring knowledge from high-resource languages to low-resource ones. Moreover, we hypothesize that annotations of syntactic dependencies can be leveraged to further facilitate cross-lingual transfer. In this work, we perform an empirical exploration of the helpfulness of syntactic supervision for crosslingual SRL within a simple multitask learning scheme. With comprehensive evaluations across ten languages (in addition to English) and three SRL benchmark datasets, including both dependency-and span-based SRL, we show the effectiveness of syntactic supervision in low-resource scenarios. Experiments Target Languages SRL Style Same Frames? Compatible Roles? Main SRL Setting EWT/UPB † ( §3.2) de,fr,it,es,pt,fi Dependency-based Yes Yes Zero-shot EWT/FiPB ( §3.3) fi Dependency-based No Yes Semi-supervised CoNLL-2009 ( §3.4) cs,zh,es,ca Dependency-based No No Semi-supervised OntoNotes ( §3.5) zh,ar Span-based No Yes Semi-supervised