Abstract
Machine Translation is the translation of text or speech by a computer with no human involvement. It is a popular topic in research with different methods being created, like rule-based, statistical and examplebased machine translation. Neural networks have made a leap forward to machine translation. This paper discusses the building of a deep neural network that functions as a part of end-to-end translation pipeline. The completed pipeline would accept English text as input and return the French Translation. The project has three main parts which are preprocessing, creation of models and Running the model on English Text.
Highlights
Machine translation (MT) is a domain of computational linguistics, which explores the use of software to translate text or speech from language to another
This work focusses on building an end to end machine translation pipeline
The datasets used for Machine Translation are from WMT
Summary
Machine translation (MT) is a domain of computational linguistics, which explores the use of software to translate text or speech from language to another. Machine translation performs substitution of words in one language for words in other, but that may not assure good translation. A more sophisticated method which is a growing field used to address the issue of recognition of multiple phrases is with statistical and neural technique. In this translation of text from one language to another, there is no human involvement and it is the machine which performs the process of conversion. There are three types of machine translation system-rules based, statistical and neural.
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