SUMMARY
For more than a decade, PMT and SMT models have dominated the field of machine translation, and neural machine translation has emerged as a new paradigm for machine translation by the 2015. Neural machine translation provides a simple modeling mechanism that is easy to use in practice and science. Thus, it does not require concepts such as word ranking, a key component of the statistical machine translation. While this simplicity may be seen as an advantage, on the other hand, the lack of careful spelling is to lose control of the translation. Even tough, the neural machine translation is more flexible in terms of translations that don’t exactly match the training data. This provides more opportunities for such models, but exempts translation from pre-determined restrictions. Failure to connect specific words can make it difficult to connect the target words you create to the original word. The widespread use of neural machine translation system has the advantage of allowing users to translate certain terms and translate uneducated data to a certain extent. In some cases, however, the structure and the grammar boundary of a sentence is often distorted. The paper is intended to address issues such as the control of neural machine translation, more accurate translation of unidentified data, the accuracy of sentence structure and grammar boundaries. To solve this problem, modern translation theory led to the hybrid model of machine translation. Our model is expansion of this hybrid model with a sentence and a grammar boundary. We named this model as hierarchical triple model (HTM).