Research map / Large language models / Retrieval and knowledge
SPARQA: Skeleton-Based Semantic Parsing for Complex Questions over Knowledge Bases
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add skeleton parsing to produce more accurate dependencies (ablation) | SPARQA · ComplexWebQuestions | P@1: 29.39 -> 31.57 | Helped |
| add sentence-level scorer (ablation) | SPARQA · ComplexWebQuestions | P@1: 26.45 -> 31.57 | Helped |
| add word-level scorer (ablation) | SPARQA · ComplexWebQuestions | P@1: 26.11 -> 31.57 | Helped |
| use SPARQA rather than PARA4QA | GraphQuestions · GraphQuestions | F1: 20.40 -> 21.53 | Helped |
| apply skeleton-based parsing | SPARQA · GraphQuestions simple questions | F1: 27.42 -> 27.68 | No clear effect |
Closest papers by meaning
- Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge (AAAI 2017 · similarity 0.90)
- Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering (AAAI 2026 · similarity 0.90)
- TrustUQA: A Trustful Framework for Unified Structured Data Question Answering (AAAI 2025 · similarity 0.86)
- FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering (AAAI 2024 · similarity 0.85)
- Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation (AAAI 2025 · similarity 0.84)
- A General Framework for Producing Interpretable Semantic Text Embeddings (ICLR 2025 · similarity 0.84)
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