# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## 1 Introduction  
With the rapid development of communication technology and economic globalization, translation between languages has become increasingly frequent, thereby drawing growing attention to machine translation (MT). Consequently, as a subtopic of artificial intelligence, MT has achieved considerable progress in recent decades. The primary goal of researchers in this field is to make the translation quality of machines closer to that of humans. Several approaches have been demonstrated to be useful in MT. Statistical MT (SMT) is one of the most widely supported approaches, whereas phrase-based SMT (PBSMT) is considered state-of-the-art MT [1]. PBSMT uses a sequence of words, aptly called \"phrases\" or \"blocks,\" rather than single words. This approach segments the source content into phrases translates phrases into the target translationunits provides more reliable quality than word-based approaches [2]. However, the traditional PBSMT models are distance-based that consider the reordering between both sides as equal, and therefore it still has many shortcomings which require in-depth investigations in various aspects. For instance, the weakness of controlling the reordering decisions [3], the data sparseness issue that becomes more prevalent while translating a highly-inflected language, and the lack of additional linguistic information of parallel corpus, which affects translation accuracy, particularly for a morphologically rich language, such as Arabic. The authors in [4] presented a factored translation model as an extension of PBSMT. This model integrates translation corpora into linguistic information, such as syntactic and morphological features, thereby achieving better translation for many language pairs. However, one of the significant problems in PBSMT is the weakness in dealing with the new form of the surface after the integration of linguistic information or features. In this work, we attempt to address the integration of these features into the source side of the model to enhance the translation of the Arabic-Chinese language pair, given the importance and necessity of these languages in economic, cultural, and global aspects. On one hand, deep learning and machine learning techniques have been widely applied and shown significant improvement in different tasks, such as speech recognition, transfer learning, load monitoring, and others. An example of this is the pipeline presented by the authors of [5] to overcome the limitations raised by the scarcity of large amounts of training data as well as time-consuming and complicated back-propagation systems. The framework is based on extreme learning machines that can predict the unknown relationship between factors of the input and output sides by using the collection of hidden units and output weights; it has reported top performance on the UK domestic appliance-level electricity (UK-DALE) dataset. As for speech recognition, the authors in [6] used Hermite polynomials to adapt the activation functions for audio modeling showing a powerful ability to learn nonlinear and more complex feature representations. The results of experiments showed that the proposed method provides a solution for data scarcity. However, the fact that computing the nonlinear transformations by using a recurrence formula can highly increase the computational load. One of the recent work on transfer learning is by the authors of [7], who proposed a transfer learning algorithm for remaining useful life prediction of a turbofan engine. This algorithm is based on bi-directional long short-term memory neural networks to deal with the difficulty of obtaining an appropriate number of samples in data-driven prognostics. The results showed that transfer learning provides an efficient performance in most cases, however, for transferring from multiple operating conditions to single operating conditions, the transfer learning led to a worse result.