Paper
Effective QA-driven Annotation of Predicate-Argument Relations Across Languages
Authors
Jonathan Davidov, Aviv Slobodkin, Shmuel Tomi Klein, Reut Tsarfaty, Ido Dagan, Ayal Klein
Abstract
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.22865v1</id>\n <title>Effective QA-driven Annotation of Predicate-Argument Relations Across Languages</title>\n <updated>2026-02-26T11:01:38Z</updated>\n <link href='https://arxiv.org/abs/2602.22865v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.22865v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <published>2026-02-26T11:01:38Z</published>\n <arxiv:comment>Accepted to EACL 2026 (Main Conference)</arxiv:comment>\n <arxiv:primary_category term='cs.CL'/>\n <author>\n <name>Jonathan Davidov</name>\n </author>\n <author>\n <name>Aviv Slobodkin</name>\n </author>\n <author>\n <name>Shmuel Tomi Klein</name>\n </author>\n <author>\n <name>Reut Tsarfaty</name>\n </author>\n <author>\n <name>Ido Dagan</name>\n </author>\n <author>\n <name>Ayal Klein</name>\n </author>\n </entry>"
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