Application of Machine Learning Combined with Wireless Network in Design of Online Translation System

The importance of translation services has become increasingly prominent with the acceleration economic globalization. Compared human translation, machine is cheaper and faster, therefore more suitable for current era. mainstream method neural which employs methods to train on parallel corpora create models. Research into yielded a wealth information. Learning generalization abilities networks have substantially enhanced effectiveness translation. This work applies learning wireless network technology build an online system real-time First, this proposes multigranularity feature fusion based directed acyclic graph, uses graph fuse different granularities as input obtain position representation. Secondly, paper improves Transformer model encoding self-attention. Then, basis features input, introduces dynamic word vectors improve embedding module, ELMo vector embeddings. Finally, builds feature-dynamic above strategy, deploys it server. Users can upload content be translated download through realize network.