  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.
