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Hierarchical Attention Flow for Multiple-Choice Reading Comprehension
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add O2P attention (options used to gather evidence) (ablation) | Hierarchical Attention Flow · RACE dev | accuracy: 46.2 -> 47.2 | Helped |
| add O2P attention (options used to gather evidence) (ablation) | Hierarchical Attention Flow · RACE-M dev | accuracy: 43.7 -> 45.3 | Helped |
| add option correlations (ablation) | Hierarchical Attention Flow · RACE dev | accuracy: 46.5 -> 47.2 | Helped |
| add sentence context encoder (ablation) | Hierarchical Attention Flow · RACE dev | accuracy: 46.9 -> 47.2 | Helped |
| add sentence context encoder (ablation) | Hierarchical Attention Flow · RACE-M dev | accuracy: 45.5 -> 45.3 | No clear effect |
| use model vs GA Reader at same 300D embeddings | GA Reader · RACE test | accuracy: 43.9 -> 46.0 | Helped |
Closest papers by meaning
- Multi-Matching Network for Multiple Choice Reading Comprehension (AAAI 2019 · similarity 0.89)
- DCMN+: Dual Co-Matching Network for Multi-Choice Reading Comprehension (AAAI 2020 · similarity 0.89)
- Multi-Mention Learning for Reading Comprehension with Neural Cascades (ICLR 2018 · similarity 0.88)
- Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions (AAAI 2019 · similarity 0.88)
- Densely Connected Attention Propagation for Reading Comprehension (NeurIPS 2018 · similarity 0.88)
- Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions (ICLR 2025 · similarity 0.84)
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