# Machine Translation in Low-Resource Languages by an Adversarial Neural Network
## 1 Introduction  
Traditional Neural Machine Translation (NMT) models directly learn and fit the correspondence between source and target language pairs through deep neural networks. This approach is based on a sequence-to-sequence (Seq2 Seq) architecture which is comprised of encoder and decoder networks. At present, the most popular NMT models such as RNNsearch [1] and Transformer [2] have designs based on the Seq2 Seq model architecture. RNNsearch has achieved remarkable translative scores due to its ability to supplement a human-like attention mechanism between the encoder and decoder. RNNsearch achieved several state-of-the-art records up to 2018 and is still widely used in machine translation today. In 2017, a novel architecture known as Transformer was introduced and outperformed existing models in different natural language processing tasks. Recently, researchers have developed a new embedding method based on Transformer, i.e., Bidirectional Encoder Representations from Transformers (BERT) [3]. However, the aforementioned approaches require a large amount of parallel bilingual data for training. For It is laborious for Low-Resource Languages (LRL) to build an adequate corpus for training satisfactory models. [PERSON] [4] systematically summarized the necessity of working on LRL information processing. In addition to linguistic diversity, models developed for LRLs can generally help strengthen the featurization, cope with overfitting problems, and facilitate useful applications. For this purpose, there has been much research focusing on LRLs. [PERSON] et al. [5]analyzed the relevance in translations by exploiting the pretrained model through the transfer encoder and decoder, but the performances of LRLs were unstable when using different High-Resource Language (HRL) models. To cope with the instability, [PERSON] et al. [6] presented a multi-round transfer learning approach, which alleviated the unpredictability of cross-lingual and generative training to some extent. Moreover, [PERSON] [7] utilized a pivot language to bridge the language pairs and train a joint network of NMT, i.e., A\(\rightarrow\)B, B\(\rightarrow\)C. [PERSON] et al. [8] introduced a triangle architecture where a small language was an intermediate variable in the translation process between rich languages, dividing the translation process into two translation processes. Their models use the rich bilingual pairs in an HRL corpus to improve the performance of LRL translation. This study presents research on adversarial learning, which achieves a higher performance in image generation [9]. It incorporates rival losses during training and can yield more explicit images. Recently, this has also been applied to NLP tasks. However, no study has investigated how adversarial learning applies to and influences LRL translation. We seek better feature extraction in the small-scale training of sentence pairs to obtain more accurate translations in complex systems. Moreover, we also take advantage of transfer learning in our proposed model to further improve NMT performance. There are some challenges to consider when attempting to implement this strategy. First, it is problematic to utilize adversary and Seq2 Seq together, as the performances of both techniques need to be analyzed and evaluated. Second, it is challenging to improve translation scores in cross-lingual transfer learning [5; 6]. Third, it is challenging to develop a new method combining a pretrained model. Therefore, the proposed system should be developed as an end-to-end differentiable model. This study proposes a novel Low resource, Adversarial, and Cross-lingual Neural Machine Translation (LAC) model for NMT. The proposed model focuses mainly on LRLs and is expected to overcome the limitations of Seq2 Seq, leverage the capabilities of multi-lingual NMT, and produce high-quality translations. To be more specific, the contributions of this study are summarized as follows:  
* A novel translation model, LAC, is designed.