Effective QA-driven Annotation of Predicate-Argument Relations Across Languages
arXiv:2602.22865v1 Announce Type: new 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
arXiv:2602.22865v1 Announce Type: new 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.
Executive Summary
This article presents a novel approach to extending predicate-argument relations annotation to multiple languages, leveraging the Question-Answer driven Semantic Role Labeling (QA-SRL) framework. The proposed cross-linguistic projection method 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. The approach is applied to Hebrew, Russian, and French, yielding high-quality training data and fine-tuned, language-specific parsers that outperform strong multilingual LLM baselines. This innovation enables efficient and broadly accessible predicate-argument parsing across languages, with significant implications for natural language processing and semantic analysis.
Key Points
- ▸ The QA-SRL framework provides a natural-language formulation of predicate-argument relations, enabling efficient and broadly accessible annotation across languages.
- ▸ The cross-linguistic projection approach reuses an English QA-SRL parser within a constrained translation and word-alignment pipeline to generate question-answer annotations.
- ▸ The method is applied to Hebrew, Russian, and French, yielding high-quality training data and fine-tuned, language-specific parsers that outperform strong multilingual LLM baselines.
Merits
Transferability of QA-SRL
The QA-SRL framework provides a transferable natural-language interface for semantics, enabling efficient annotation across languages.
Improved annotation efficiency
The cross-linguistic projection approach enables automatic generation of high-quality question-answer annotations, reducing the need for costly manual annotation efforts.
Demerits
Language-specific parser training
The proposed approach requires training of language-specific parsers, which may be time-consuming and computationally expensive.
Limited applicability to low-resource languages
The approach may not be directly applicable to languages with limited resources, requiring additional adaptation and fine-tuning.
Expert Commentary
The article presents a significant innovation in the field of natural language processing, leveraging the QA-SRL framework to extend predicate-argument relations annotation to multiple languages. The proposed cross-linguistic projection approach demonstrates the potential of this framework for improving annotation efficiency and enabling broader accessibility. However, the approach's limitations, particularly in low-resource languages, highlight the need for further research and adaptation. As the field continues to evolve, the proposed approach will likely play a key role in shaping the development of natural language processing and semantic analysis tools.
Recommendations
- ✓ Future research should focus on adapting the proposed approach to low-resource languages, addressing the challenges associated with training language-specific parsers.
- ✓ The QA-SRL framework should be explored as a potential interface for other NLP tasks, such as text classification and sentiment analysis, to leverage its transferability and efficiency advantages.
