This study presents linguistically augmented models of phrase-based statistical machine translation (PBSMT) using different linguistic features (factors) on the top of the source surface form. The architecture addresses two major problems occurring in machine translation, namely the poor performance of direct translation from a highly-inflected and morphologically complex language into morphologically poor languages, and the data sparseness issue, which becomes a significant challenge under low-resource conditions. We use three factors (lemma, part-of-speech tags, and morphological features) to enrich the input side with additional information to improve the quality of direct translation from Arabic to Chinese, considering the importance and global presence of this language pair as well as the limitation of work on machine translation between these two languages. In an effort to deal with the issue of the out of vocabulary (OOV) words and missing words, we propose the best combination of factors and models based on alternative paths. The proposed models were compared with the standard PBSMT model which represents the baseline of this work, and two enhanced approaches tokenized by a state-of-the-art external tool that has been proven to be useful for Arabic as a morphologically rich and complex language. The experiment was performed with a Moses decoder on freely available data extracted from a multilingual corpus from United Nation documents (MultiUN). Results of a preliminary evaluation in terms of BLEU scores show that the use of linguistic features on the Arabic side considerably outperforms baseline and tokenized approaches, the system can consistently reduce the OOV rate as well.

Morphologically rich and complex languages such as Arabic, pose a major challenge to neural machine translation (NMT) due to the large number of rare words and the inability of NMT to translate them. Unknown word (UNK) symbols are used to represent out-of-vocabulary words because NMT typically operates with a fixed vocabulary size. These rare words can be effectively encoded as sequences of subword units by using algorithms, such as byte pair encoding (BPE), to tackle the UNK problem. However, for languages with highly inflected and morphological variations, such as Arabic, the aforementioned method has its own limitations that make it not effective enough for translation quality. To alleviate the UNK problem and address the inconvenient behavior of BPE when translating the Arabic language, we propose to utilize a romanization system that converts Arabic scripts to subword units. We investigate the effect of our approach on NMT performance under various segmentation scenarios and compare the results with systems trained on original Arabic form. In addition, we integrate Romanized Arabic as an input factor for Arabic-sourced NMT compared with well-known factors, namely, lemma, part-of-speech tags, and morph features. Extensive experiments on Arabic-Chinese translation demonstrate that the proposed approaches can effectively tackle the UNK problem and significantly improve the translation quality for Arabic-sourced translation. Additional experiments in this study focus on developing the NMT system on Chinese-Arabic translation. Before implementing our experiments, we first propose standard criteria for the data filtering of a parallel corpus, which helps in filtering out its noise.

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