SORTIE: Dependency-Aware Symbolic Reasoning for Logical Data-to-text Generation (2023.findings-acl)
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| Challenge: | Existing studies on logical data-to-text generation rely on neural language models to generate the final table description, but they have difficulty working out key entities in the description. |
| Approach: | They propose a symbolic reasoning framework that reasons out each entity in the table description with a table-compatible programming language. |
| Outcome: | The proposed framework outperforms existing methods on three datasets and three backbones with an absolute improvement of 5.7%11.5% on SP-Acc. |
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