Abstract:
Machine translation is a challenging task that traditionally involves large statistical models developed using highly sophisticated linguistic knowledge. Neural machine translation is the use of deep neural networks for the problem of machine translation. The use of Machine Translation for the linguistic processing is taking up pace throughout the world. The demand of broader communication devices is increasing considerably since the world has become a single global hub of information. Bearing this in mind, the need for an AI driven solution, to provide this service, has risen exponentially.
“Semantic Neural Translation Device” provides on a semantic neural networks-based device which will use contextual data to interpret the complete meaning in text and translate it from one language to another, in this project, from English Language to German Language due to the availability of datasets online. Translators based on classical Natural Language Processing methods already exist, but the improved translation method, based on what context the text is being written, is still scarce.
This approach is quite new and only a handful of organization and institutions are implementing it on a large scale. The market leaders in this work at the moment would be Google, Facebook, Amazon and Microsoft, but their work it private and not accessible for learning or other purposes. Work still needs to be done to build a state-of-the-art semantic machine which translates a text based on the context it is being used in. The device can be extended to provide translations from any language of the world given the dataset needed for training. This device proves extremely useful to the global organizations and also to the local ones as it will aid in expanding new horizons of market and communication across the globe.
