Research map / Large language models / Language, safety and interpretability
Semantic Parsing with Neural Hybrid Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add neural window features to discriminative hybrid tree (J=2) | DISCHT+ · GeoQuery English | accuracy: 86.8 -> 90.0 | Helped |
| add neural features with J=0 (dense embedding replaces sparse words) (ablation) | DISCHT+ · GeoQuery English | accuracy: 86.8 -> 87.9 | Helped |
| increase window size from 1 to 2 (ablation) | Neural hybrid tree · GeoQuery German | accuracy: 76.8 -> 73.9 | Hurt |
| increase window size from 1 to 2 (ablation) | Neural hybrid tree · GeoQuery Chinese | accuracy: 75.4 -> 81.1 | Helped |
| increase window size from 1 to 2 (ablation) | Neural hybrid tree · GeoQuery Thai | accuracy: 84.6 -> 82.1 | Hurt |
| add neural features (best J per language) | DISCHT+ · GeoQuery additional languages | accuracy: 76.1 -> 81.1 (Chinese) | Helped |
| add neural features under synonym replacement | DISCHT+ · GeoQuery synonym (avg over 8 languages) | F1 drop: -1.6 -> -1.2 | Helped |
Closest papers by meaning
- A Neural Transition-Based Approach for Semantic Dependency Graph Parsing (AAAI 2018 · similarity 0.88)
- Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning (AAAI 2020 · similarity 0.88)
- Discontinuous Constituent Parsing with Pointer Networks (AAAI 2020 · similarity 0.86)
- Global Greedy Dependency Parsing (AAAI 2020 · similarity 0.86)
- BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing (NeurIPS 2023 · similarity 0.86)
- Improving Unsupervised Constituency Parsing via Maximizing Semantic Information (ICLR 2025 · similarity 0.85)
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