# A Mongolian-Chinese Neural Machine Translation Method Based on Semantic-Context Data Augmentation
## 1 Introduction  
Data augmentation (DA) is a technology that employs relevant methods to expand data when the training data are limited. It has shown remarkable effects in various deep learning and machine learning tasks. While extensively and successfully applied in the field of computer vision [1], there is no in-depth research on natural language processing (NLP) and there has been limited success in achieving performance improvements. The existing data augmentation methods can generally be divided into two categories: one is word-replacement methods, and the other is relying on translation models for DA. The first method is mainly to replace the words in the training data to achieve the effect of DA. [PERSON] et al. [2] improved the quality of sentence translation by replacing high-frequency words with low-frequency words in language models. [PERSON] et al. [3] proposed a universal framework for implementing data augmentation, which can obtain data from rich to scarce languages. [PERSON] et al. [4] proposed a method for enhancing \"soft\" contextual data by randomly selecting words and then obtaining \"soft words\" closely related to the context of the word through a language model. [PERSON] et al. [5] reordered the target sentence to match the order of the source language and used it as an additional source for training-time supervision. [PERSON] et al. [6] proposed a causal relationship based on language models and phrase alignment, which constructs a pseudo-parallel corpus by generating counterfactual aligned phrases. For the second method, the DA effect is mainly achieved by generating pseudo-parallel corpora through translation models. [PERSON] et al. [7] were the first to propose the use of monolingual corpora for data augmentation. [PERSON] [2] and [PERSON] [8] used back-translation methods to reverse-translate monolingual data from the target language, generating more source-language-enhanced versions. [PERSON] et al. [9] proposed areverse-translation method with labels, which labels real data and pseudo-data differently, so that the model can effectively distinguish these two types of data. [PERSON] et al. [10] demonstrated through experiments that adding a certain proportion of noise to the generated pseudo-data can effectively improve translation quality. [PERSON] et al. [11] proposed a bilingual data extraction method called \"Extract Edit\" for generating high-quality bilingual data through substitution and back-translation. [PERSON] et al. [12] proposed a method of alternating training with real data and comprehensive data, which significantly improved the performance of translation. [PERSON] et al. [13] used reverse translation to generate pseudo-bilingual data by separately generating source and target languages from third-party single-axis languages. [PERSON] et al. [14] proposed using self-learning algorithms to generate pseudo-parallel corpora from monolingual data, and also using two NMT models in a multitasking learning framework to predict translations and reorder source sentences. [PERSON] et al. [15] proposed an improvement on self-learning methods, namely iterative self training methods. [PERSON] et al. [16] improved the performance of NMT by iterating reverse translation in high- and low-resource situations. [PERSON] et al. [17] established adversarial networks to improve the performance of NMT models. [PERSON] et al. [18] introduced a generative adversarial network into unsupervised machine translation, mapping the distribution of source and target words into a shared semantic space. [PERSON] et al. [19] proposed a new data augmentation model--continuous semantic augmentation--to improve translation quality. However, there are few studies on DA methods in Mongolian neural machine translation (MNMT). The structure of Mongolian sentences takes the subject-object-predicate form, which is different from most target languages in vocabulary, grammar and rhetorical devices [20]. Therefore, the development of MNMT has been restricted by the scarcity of parallel corpora for a long time.