Effective QA-driven Annotation of Predicate-Argument Relations Across Languages
This work provides a more efficient and accessible method for creating predicate-argument parsing resources for new languages, which is crucial for researchers and developers working on interpretable semantic analysis in diverse linguistic contexts.
This paper addresses the challenge of extending semantic predicate-argument relation annotation beyond English by introducing a cross-linguistic projection approach. This method reuses an English QA-SRL parser to automatically generate question-answer annotations for Hebrew, Russian, and French, resulting in language-specific parsers that outperform strong multilingual LLM baselines like GPT-4o and LLaMA-Maverick.
Explicit representations of predicate-argument relations form the basis of interpretable semantic analysis, supporting reasoning, generation, and evaluation. However, attaining such semantic structures requires costly annotation efforts and has remained largely confined to English. We leverage the Question-Answer driven Semantic Role Labeling (QA-SRL) framework -- a natural-language formulation of predicate-argument relations -- as the foundation for extending semantic annotation to new languages. To this end, we introduce a cross-linguistic projection approach that reuses an English QA-SRL parser within a constrained translation and word-alignment pipeline to automatically generate question-answer annotations aligned with target-language predicates. Applied to Hebrew, Russian, and French -- spanning diverse language families -- the method yields high-quality training data and fine-tuned, language-specific parsers that outperform strong multilingual LLM baselines (GPT-4o, LLaMA-Maverick). By leveraging QA-SRL as a transferable natural-language interface for semantics, our approach enables efficient and broadly accessible predicate-argument parsing across languages.