In this paper, we propose an enhanced version of the vanilla transformer for data-to-text generation and then use it as the generator of a conditional generative adversarial model to improve the semantic quality and diversity of output sentences. Specifically, by adding a diagonal mask matrix to the attention scores of the encoder and using the history of the attention weights in the decoder, this enhanced version of the vanilla transformer prevents semantic defects in the output text. Also, using this enhanced transformer along with a triplet network, respectively, as the generator and discriminator of conditional generative adversarial network, diversity and semantic quality of sentences are guaranteed. To prove the effectiveness of the proposed model, called conditional generative adversarial with enhanced transformer (CGA-ET), we performed experiments on three different datasets and observed that our proposed model is able to achieve better results than the baselines models in terms of BLEU, METEOR, NIST, ROUGE-L, CIDEr, BERTScore, and SER automatic evaluation metrics as well as human evaluation.

中文翻译：

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使用条件生成对抗和增强型转换器生成数据到文本

在本文中，我们提出了用于数据到文本生成的普通变压器的增强版本，然后将其用作条件生成对抗模型的生成器，以提高输出句子的语义质量和多样性。具体来说，通过将对角掩码矩阵添加到编码器的注意力分数并使用解码器中注意力权重的历史记录，这种增强版本的普通变压器可以防止输出文本中的语义缺陷。此外，使用这种增强型变压器和三元组网络分别作为条件生成对抗网络的生成器和判别器，保证了句子的多样性和语义质量。为了证明所提出的模型（称为增强型变压器条件生成对抗模型（CGA-ET））的有效性，我们在三个不同的数据集上进行了实验，并观察到我们提出的模型在 BLEU 方面能够比基线模型取得更好的结果， METEOR、NIST、ROUGE-L、CIDEr、BERTScore 和 SER 自动评估指标以及人工评估。