Advance Research on Neural Machine Translation Integrating Linguistic Knowledge

Although neural machine translation has become the mainstream method and paradigm in the current research and application of machine translation, there are also some problems such as the fluent but not faithful of the translation results, difficult processing of rare words, poor performance of low-resource languages, poor cross-domain adaptability, and low prior knowledge utilization. Inspired by statistical machine translation research, incorporating linguistic information into neural machine translation models, using existing linguistic knowledge, alleviating the inherent difficulties faced by neural machine translation and improving translation quality has become a hot topic in the field of neural machine translation research. According to the grammatical unit's classification system, the research in this area can be divided into three categories: neural machine translation incorporating character or word structure information, neural machine translation incorporating phrase structure information, and neural machine translation incorporating syntactic structure information. The current research also focuses on these three aspects. On the basis of sorting out the main challenges and reasons faced by neural machine translation, this paper focuses on each type of research to introduce its core ideas and functions, status and main results, problems and development trends. Finally, it summarizes the challenges that still exist in this field and looks forward to future research direction