Generative Adversarial Networks (GAN) offer a promising approach for Neural Machine Translation (NMT). However, feeding multiple morphologically languages into a single model during training reduces the NMT's performance. In GAN, similar to bilingual models, multilingual NMT only considers one reference translation for each sentence during model training. This single reference translation limits the GAN model from learning sufficient information about the source sentence representation. Thus, in this article, we propose Denoising Adversarial Auto-encoder-based Sentence Interpolation (DAASI) approach to perform sentence interpolation by learning the intermediate latent representation of the source and target sentences of multilingual language pairs. Apart from latent representation, we also use the Wasserstein-GAN approach for the multilingual NMT model by incorporating the model generated sentences of multiple languages for reward computation. This computed reward optimizes the performance of the GAN-based multilingual model in an effective manner. We demonstrate the experiments on low-resource language pairs and find that our approach outperforms the existing state-of-the-art approaches for multilingual NMT with a performance gain of up to 4 BLEU points. Moreover, we use our trained model on zero-shot language pairs under an unsupervised scenario and show the robustness of the proposed approach.

翻译：生成对抗网络（GAN）为神经机器翻译（NMT）提供了一种有前景的方法。然而，在训练过程中将多种形态语言输入单一模型会降低NMT的性能。在GAN中，类似于双语模型，多语言NMT在模型训练时仅考虑每个句子的单个参考译文。这种单一参考译文限制了GAN模型从源语句表示中学习足够信息的能力。因此，本文提出一种基于去噪对抗自编码器的句子插值（DAASI）方法，通过学习多语言对源句和目标句的中间潜在表示来实现句子插值。除潜在表示外，我们还利用Wasserstein-GAN方法处理多语言NMT模型，将模型生成的多种语言句子纳入奖励计算。这种计算得到的奖励有效优化了基于GAN的多语言模型的性能。我们在低资源语言对上进行了实验，发现我们的方法在性能上优于现有最先进的多语言NMT方法，BLEU值最高提升4个点。此外，我们在无监督场景下将训练后的模型应用于零样本语言对，展示了所提方法的鲁棒性。