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

Abstract

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