Papers by Lihui Zhang
KE-GCL: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering (2022.findings-emnlp)
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| Challenge: | Existing models for commonsense question answering lack effective representations of knowledge graphs. |
| Approach: | They propose a Knowledge Enhanced Graph Contrastive Learning model by incorporating contextual descriptions into QA pairs and adopting a graph contrastive learning scheme. |
| Outcome: | The proposed model outperforms existing methods consistently on two benchmark datasets. |
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems (2020.emnlp-main)
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| Challenge: | Existing models focus on one-unknown linear MWPs. |
| Approach: | They propose a universal expression tree-structured solver that integrates multiple expression trees underlying a MWP into a single expression tree. |
| Outcome: | The proposed method outperforms state-of-the-art models on a MWPs dataset and generates a universal expression tree explicitly by deciding which symbol to generate . |