Completely automatic machine translation still has a long way to go to achieve professional quality, particularly in the challenging language pairs such as Japanese and English. QQ Trend has been working with a professional translation company to use the latest machine learning technology and the latest NLP technology to improve its efficiency. In other words, this is machine learning in partnership with a human professional—not trying to replace them.
Darren Cook demonstrates how to use LSTMs to tackle translation, focusing on one of the most difficult language pairs: Japanese to English. Darren covers the core technologies, practical issues when integrating them with the latest tokenizers, dictionaries, structured data sources, unstructured data sources, and customer style sheets, and the solution’s performance and platform portability. He also explains how to adapt the solution to new terminology or the translator’s preferred vocabulary and writing style.
Darren Cook is a director at QQ Trend, a financial data analysis and data products company. Darren has over 20 years of experience as a software developer, data analyst, and technical director and has worked on everything from financial trading systems to NLP, data visualization tools, and PR websites for some of the world’s largest brands. He is skilled in a wide range of computer languages, including R, C++, PHP, JavaScript, and Python. Darren is the author of two books, Data Push Apps with HTML5 SSE and Practical Machine Learning with H2O, both from O’Reilly.
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