Papers with fact-level relation modeling leveraging graph convolution network
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering (2021.findings-acl)
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| Challenge: | Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process. |
| Approach: | They propose a framework to exploit more valid facts while obtaining explainability for multi-hop question answering at web scale by dynamically constructing a semantic graph and reasoning over it. |
| Outcome: | The proposed framework surpasses existing approaches while maintaining high explainability on OpenBookQA and ARC-Challenge. |