Rough set machine translation using deep structure and transfer learning

Abstract

Machine translation is one of the parts of language processing within linguistic computing for automatic translation from one language to another. The paper introduces one of the most critical areas of soft computing and natural language processing, i.e. machine translation technique that is based on deep model structure of rough sets with capability to transfer learning. A deep rough set learning is developed to support machine translation to recognize and translate tens of thousands of words/sentences automatically. To our knowledge, this is the first attempt aiming to use rough sets in machine translation rather than Arabic language translation. A deep information table is learned by assigning the morphemes-similar objects with similar learning complexities into same class and it can identify the inter-related learning tasks automatically. To account for the differences among source-languages domains, we proposed a partial transfer learning scheme in which only part of source information is transferred. The experiments have demonstrated that the proposed model can achieve competitive results and significantly outperformed other methods for translation on both accuracy rates and the efficiency for machine translation.