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What is Data To Text Generation Using Conditional Generative Adversarial With Enhanced Transformer about?

This paper presents a novel model called conditional generative adversarial with enhanced transformer (CGA-ET) for data-to-text generation, which improves semantic quality and diversity of generated sentences. The model enhances the vanilla transformer by incorporating a diagonal mask matrix and utilizing a triplet network as a discriminator within a conditional generative adversarial framework. Experimental results demonstrate that CGA-ET outperforms baseline models across various automatic evaluation metr

Author

aslampakinfo

Language

EN