# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## 6 Results and Analysis
Although Arabic morphological analyzer is helpful in MT, it provides complicated pre-processing making the system training cumbersome which may increase the incidence of the dropping problem. Factored models choose to include most of these words taking advantage of the syntactic structures that enrich the training pipeline by good coherent sentences. Sentence 3 in Figure 5 shows an example where the words \"eliminate those obstacles\" were dropped by the baseline system, and in sentence 2 the words \"have evolved over time\" are dropped by both tokenized models. Unlike the baseline and tokenized models, factored models take those words into consideration. Overcome the sparseness problem  
The tokenized and factored models help decrease the OOV rates, while the baseline model suffers from this problem thereby affects the performance of the translation results. We explain this to the morphological complexity and lexical diversity of Arabic, where the morphological analysis tool helps by providing a list of word-level analyses as well as splitting prefixes and suffixes, and removing diacritization that should increase the matching accuracy. The use of linguistic features provides us new word forms such as lemma, improves the generalization ability and allows the decoder to use multiple translation options based on MDP or backoff model. As an example, in sentence 3 of Figure 5 the diacritized word \"plans\" mapped to OOV by the baseline model, whereas removing the diacritization in the tokenized models helped the decoder to find the translation. In the lemma backoff model the decoder had two options to provide the output, which are surface-level and lemma-level phrase tables, during the translation, if the decoder didn't find the surface form, it goes back to its lemma form to gain the translation, and therefore decrease OOV rates. The improvement of D3* scheme  
Since that Chinese doesn't have the definite article \"the\"; removing the definite article by the D3* model makes Arabic as a source language more similar to Chinese as a target language that improves the alignment and decreases the OOV rates which, likewise, gains a better performance. As in the examples of Figure 5, where removing \"the\" from the words \"the state, the necessary\" in sentence 1 and \"the government\" in sentence 3 enhanced the translation performance of D3* model. The advantages of linguistic factors  
From the selected examples, we notice that the factored models obtained better quality and grammatical performance, due to the strength of the linguistic features which helps to characterize the words in sentence perfectly. POS tags help to best exploit the data which considerably reduce the number of possible options, and promotes the ability of disambiguation for a token that has a different meaning in the context. Morph features improve generalization ability taking benefits from the rich knowledge for each token in the context. As for lemma Backoff and multiple decoding paths lemma/morph, both models show considerable results in handling the OOV issues, especially for the translation from a morphologically complex language as Arabic. The evaluation results answer our empirical question that using linguistic factors on the Arabic side improves the quality of Arabic-to-Chinese translation. Further, different configurations of the factored model provide better translation quality compared with the baseline and better or comparable performance with both tokenized models.