A Proposed Sequence-to-Sequence Modeling for Arabic Dialect Machine Translation

Neural machine is considered an innovative approach to make translation, making statistical machine model for translation that rely only on neural networks. The basic translation models using neural machine often contain of encoder operations and a decoder operation. The encoder makes cuttings a fixed-length sentence representation from a variable-length sentence that are input for translation, but the decoder produces right translation for this fixed-length representation. This paper motivates to make compare between recurrent neural traditional networks that are recursive based (RNN), enhanced once long short-term memory unit (LSTM) and sub type of LSTM that are gated recurrent unit (GRU). The process sequence-to-sequence labelling check recurrent unit's types to make translating between language pairs, for our study from Arabic Levantine to English. The perplexity measure used to score effect the trained model. BLEU scores used to measure quality translation.