MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation (2023.findings-acl)
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| Challenge: | MURMUR generates highly faithful and correct reasoning paths that lead to 26% more logically consistent summaries on LogicNLG compared to direct prompting. |
| Approach: | They propose a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning that generates reasoning paths using neural and symbolic modules with specific linguistic and logical skills. |
| Outcome: | The proposed method improves on two data-to-text generation tasks, while achieving comparable performance to fine-tuned GPT-2 on out-of-domain data. |
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