Hybrid machine translation outperforms rule-based and statistical approaches, unlocking new levels of accuracy and fluency.
The article argues that combining Statistical Machine Translation and Rule-Based Machine Translation is more effective than using them separately. This hybrid approach improves the translation of long, complex sentences. The study explores different methods of machine translation, such as SMT, RBMT, PBMT, and EBMT, analyzing their strengths and weaknesses. Various tools like Moses toolkit and language modeling toolkit were used for analysis. The article also discusses word normalization, alignment, and sentence segmentation principles.