# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## 1 Introduction
Another line of work in low-resource scenarios is speech-to-text translation (ST) that has been proved valuable, for instance in the documentation of unwritten or endangered languages. Traditional ST presents a pipeline of automatic speech recognition (ASR) and MT where transcribed source audio and parallel text are required to train ASR and MT, respectively. In [8], the authors proposed a pipeline to extract information from speech signals to produce ST alignments where the speech of source language (as an endangered language) was annotated with translations than with transcriptions. This pipeline aids in Computational Language Documentation (CLD), and hence it provides valuable resources for the training of ASR systems. As the number of unwritten or endangered languages is so large, the documenting of these languages is becoming an interesting area for research. One of the recent efforts in this area is the project breaking the unwritten language barrier (BULB) [9] which focuses on collecting spoken translations along with a speech by analyzing and annotating audio recordings using automatically discovered linguistic units (phones, morphs, words, grammar, etc.). An example of a speech translation dataset is the Fisher and Callhome Spanish-English corpora [10] that contains transcribed and translated telephone conversations of Spanish to English. Despite the medium size and low-bandwidth recordings of this corpora, it has been used in many previous works dealing with Spanish as a low-resource language which makes it an appropriate language to work with. However, to make ASR systems perform reasonably well, a large amount of annotated data is needed. Many languages lack available speech data or the annotations required to train ASR; according to [9] only about 1% of the world's languages have the minimum amount of available data. Furthermore, using a system trained for a different or related language (as cross-language) to build ASR system for low-resource languages leads to quite poor performance [11]. Due to the scarcity of available documentation scenario that matches Arabic and Chinese as well as the expensive process and the required knowledge of domain to obtain hand-labeled training data, we leave the investigation of ASR technology between this language pair for future work, since this area is bringing more questions than answers. In these scenarios, a direct MT system is appealing. Arabic-Chinese MT is a challenging task because they are low-resources languages that belong to different families. Arabic is a morphologically rich language with many morphological features, such as affixes, stems, roots, and patterns [12]. Also due to the highly-inflected words in Arabic, the well-known issues of missing words and data sparsity arise; and hence affect the accuracy of translation and leads to more out-of-vocabulary (OOV) words [13]. By contrast, Chinese is written using logograms and lacks linguistic morphology [14]. In this study, we propose an approach that takes advantage of the rich linguistic information of Arabic to improve translation quality between Arabic (as the source side) and Chinese (as the target side). First, we introduce a baseline model of our experiments using the default settings of PBSMT. To solve the sparse data problems, we apply multiple optimizing tokenization schemes to the preprocessing steps in later experiments and then compare their performance. Finally, we inject several linguistic factors into Arabic to create an optimized factored model. The factors used in our experiments include surface form, lemma, part-of-speech (POS), and morphological features (morph). The morph features such as case, state, gender, gloss, person, number, voice, and mood. We investigate the effects of these factors individually and on combination models. Translation models with general representations such as lemma form provide improved generalization based on the inflectional variants for the same word.