# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## 2 Related Works  
Previous work on MT between Arabic and Chinese is unexpectedly scarce, despite the economic and global prevalence of these languages. Chinese has approximately 1.2 billion native speakers, whereas Arabic has nearly 400 million. Such values make Arabic-Chinese MT an important and attractive field in linguistics. Two aspects may explain the limited research on MT on this language pair: (a) the scarcity of freely available parallel corpora; and (b) the morphological differences and complexity that require linguistic knowledge. In the current work, we discuss related studies in this field. The first work was proposed by [18] who compared between the direct approach of SMT and the pivot-based approach using English as the pivot language in translating Arabic into Chinese. The result showed that the pivot-based approach performed better than the direct translation. Although the pivoting strategy provides a solution to the lack of parallel data, it causes information loss when using a morphologically poor language, such as English, as a pivot language [19]. In [20], the authors collected a corpus from United Nations (UN) documentation and used it to build standard PBSMT systems of Arabic-Chinese and Chinese-Arabic translations. The authors in [21] compared between SMT and NMT across 30 translation directions including Arabic-Chinese but did not conduct any individual preprocessing for Arabic. In [22], the authors evaluated SMT performance using several tokenization schemes of MADAMIRA for Arabic (as source language) translated into Chinese and other languages, the results of this framework showed that using Arabic pre-processing enhanced the translation and helped to overcome the data sparsity problem. However, all previous studies on this language pair have presented without using any linguistic features, which have been proven valuable in the MT field. We address this limitation in this research. Studies that used linguistic annotation have been performed in different languages (A large amount of studies have been done on this topic. Here we only review some example works) with annotation factors such as lemma, POS, morph, and others. The authors in [23] explored the difficulties of integrating linguistic features into PBSMT model. In [24], the authors achieved improvements by adding linguistic annotations to English (as the source side) translating into Greek and Czech, where the proposed models reduced the error of noun case agreement and verb conjugation. The authors in [25] discussed the benefits of enriching the input with morphological features to enhance the translation from English into Hindi and Marathi; the results showed that the integrated models reduced the number of OOV words and improved the fluency of the translation. In [26], the authors created factored translation models from English to Slovenian and compared the results with non-factored approaches; their experiments showed that the factored approach is better than the non-factored while translating complex texts. In [27], the authors discussed the benefits of annotation features for several European language pairs, in which the experiments showed that factored models have better results in enhancing the translation quality. Studies on factored translation models between Arabic or Chinese and other languages are few. For instance, the authors in [28] used additional features to create English-Arabic factored models and compare the performance with segmented models, the factored models achieved better quality. In [29], the authors developed a factored approach that improved Arabic-English translation by injecting POS and combinatory categorial grammar (CCG) into English, where factored models gained better quality enhanced the grammatical performance. The authors in [30] presented the results of PBSMT and a factored translation model between Korean and Chinese, in which the factored model performed a better MT quality compared to the standard PBSMT model.