Semantic Evaluation of Multilingual Data-to-Text Generation via NLI Fine-Tuning: Precision, Recall and F1 scores (2025.findings-acl)
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| Challenge: | KG-to-Text models are prone to errors like Additions and Omissions, and few languages are taken into account since both train and test data are not readily available. |
| Approach: | They propose a multilingual evaluation framework that is reference-less . it allows estimating how much a KG-to-Text Model under- (omission) or over- (addition) generates. |
| Outcome: | The proposed evaluation framework outperforms prior reference-less metrics in correlation with human judgments and provides scores for precision and recall. |
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