# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## Challenges and Approach
### Linguistic Input Features  
Many previous works have demonstrated the benefits of the factored model as an extension of PBSMT between different languages, which annotates the source side and/or target side with linguistic features. In this work, we focus on the annotation of the source side (i.e., Arabic) using some popular linguistic features to determine whether these features will improve the quality of Arabic \(\rightarrow\) Chinese translation. We use MADAMIRA, which analyzes the Arabic context by applying rule-based and supervised learning techniques to implement the morphological features of the input word. Several types of linguistic features can be obtained by MADAMIRA for Arabic. Here, we discuss additional details about the individual factors we use in our experiments. **Lemma:**  
Arabic is a highly inflected language, which makes Arabic lemmatization important in the preprocessing step to enhance the information extraction results. The inflections of Arabic words are generated by adding prefixes, suffixes, and vowels to the root. For example, the word \(\texttt{+j,j,i=j}\texttt{(wsyxbrwnhA)}\) \"and they will tell her\" has the prefix (ws) \"and will\" and suffix (wnhA) \"they her\", which are both attached to the root (xbr) that basically means \"telling.\" This word also has the stem (yxbr) \"tell\" and the lemma (xbr) \"the concept of telling\". Given its language complexity, lemma exhibits better performance in Arabic information retrieval compared with the root and stem. The root leads to low precision, e.g., differences exist between the stem for broken plurals and their singular patterns. Moreover, imperfect verbs have different stems with their perfect verbs. Several natural language processing (NLP) tools can be used to output lemmas for Arabic words. In this work, we use MADAMIRA morphological lemmatizer. The reported lemmatization accuracy for modern standard Arabic by the MADAMIRA system is 96.2%, and it has been evaluated on a dataset extracted from the Penn Arabic Treebank. Each Arabic token in our corpora has a diacritized lemma, as shown in the example of Table 1.  
**POS:**  
POS tags play an essential role in different NLP tasks, such as MT. These tags provide the linguistic knowledge and syntactic role of each token in the context, which helps in information extraction and reduces data ambiguity. The basic POS for Arabic words has many categories: noun, e.g., \(\vdash\)\(\exists\)5(ktAb) \"book\" or \(\vdash\)\(\exists\)5(ktb) \"books\" in plural form and \(\vdash\)\(\exists\)5(mktwb) \"writing or message;\" verb, e.g., \(\vdash\)\(\exists\)5(yktb) \"write\" or \(\vdash\)\(\exists\)5(ktb) \"wrote\" in past form; adjective, e.g., \(\vdash\)\(\exists\)5(mktwb) \"written or fated;\" and particle, e.g., \(\vdash\)\(\exists\)(mn) \"from\" or \(\vdash\)\(\exists\)(Aly) \"to.\"  
From the preceding examples, we can see that the word \(\vdash\)\(\exists\)5(ktb) \"books\" can be used as a plural noun and as the verb \"wrote\" in past form. Meanwhile, the word \(\vdash\)\(\exists\)5(mktwb) can be used as a noun \"message\" or an adjective \"fated\". In such situation, POS tagging helps analyze and distinguish word meaning, which is optically called \"word sense\" in the translation corpora. In this work, we extract POS tags for the input tokens of Arabic by using the MADAMIRA morphological analyzer, which has been considered a state-of-the-art Arabic tagger with a POS accuracy of 96.91% [38]. As shown in Table 1, the results of the MADAMIRA tagger annotate each word by its POS tag. **Morph Features:**  
MT approaches suffer from data sparseness problems when translating into or from morphologically rich and complex languages, such as Arabic. Thus, morphology analysis is necessary to handle data sparseness and improve translation quality. Different word types in the Arabic language have various sets of morph features. For example, verbs have person, gender, number, voice, and mood, whereas nouns have case, state, gender, gloss, number, and the attached prolitic DET.