# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## 5 Experiments Settings
### Chinese Segmentation
#### 5.3.2 Factored Models  
This part discusses the core of this work. We annotate the Arabic side by adding linguistic features on top of the surface forms in the translation model when translating Arabic into Chinese to optimize the translation results. These features provide rich representation and sufficient knowledge for nearly any required transformation. In this framework, we use three features (lemma, POS, and morph) for Arabic to conduct several experiments using different settings based on these features. Each word in Arabic factored corpus is a vector of features that represent information levels. For Chinese, we use Chinese segmented words.  
**Surface+Lemma:** This model incorporates diacritized lemma into the input side to reduce data sparseness and provide improved generalization based on the inflectional variants for the same word. Mapping lemma and surface onto surface enhances performance.  
**Surface+POS:** The translation option of this model is based on adding POS tags to the input corpus. POS tags help in disambiguation and in extracting additional information about the data.  
**Surface+Morph:** To obtain better data representation and linguistic knowledge, we inject an input word using several morph features which are case, state, gender, gloss, number, person, voice, and mood. These features improve generalization ability.  
**Surface+Lemma+POS:** In this integrated model, we incorporate lemma and POS features to solve the data sparseness and disambiguation problems and evaluate whether multiple factors also increase translation performance.  
**Surface+Lemma+Morph:** Here, we combine two features (lemma and morph) on top of the surface form. The motivation is to obtain more flexible notions and improve the precision of the translation output.  
Figure 3: Step-wise result of the Penn Arabic Treebank (ATB) approach, the red letters are normalized and tokenized by the approach, Chinese pinyin is presented to ease readability.  
**Surface+POS+Morph:** The word in this model represents the vectors of the POS tags and morph features to enrich the input through additional knowledge, thereby solving the data sparseness problem and enhancing generalization.  
**Surface+All Features:** We also test the effect of integrating all available features (lemma, POS, morph) to determine whether the addition of such rich information will improve the translation quality of this language pair.  
**Multiple Decoding Paths Lemma/Morph:** The factored model allows the use of multiple paths in parallel; that is, translation options originate from different phrase tables. We set two models in this model. First, the surface-level model maps the surface onto the surface. Second, the lemma/morph model provides morphological analysis. Translation options from multiple tables compete in the decoding step. When the same translation is found in different tables, varying scores are used to create translation options for each occurrence. The translation model that uses the multiple decoding path (MDP) strategy becomes more robust; hence the input sentence is translated with higher probability.  
**Lemma Backoff:** For the translation of morphologically rich and complex languages, such as Arabic, into simpler languages, such as Chinese, translating lemmas instead of the words that have not observed in the training corpus is useful. This strategy is called the backoff model. In contrast to MDP, the decoder in the backoff model finds one phrase in different phrase tables.  
The first table is used as a priority table, whereas the second table is a backoff table for translations that are not found in the first one. In this framework, the surface level is a priority phrase table, whereas the lemma-level phrase table is used as a backoff phrase table, as shown in Figure 4. This model helps decrease out-of-vocabulary rates and enhances translation quality.