Deep learning in machine translation

Abstract

Machine translation (MT) is an important natural language processing task that investigates the use of computers to translate human languages automatically. Deep learning-based methods have made significant progress in recent years and quickly become the new de facto paradigm ofMTin both academia and industry. This chapter introduces two broad categories of deep learning-based MT methods: (1) component-wise deep learning for machine translation that leverages deep learning to improve the capacity of the main components of SMT such as translation models, reordering models, and language models; and (2) end-to-end deep learning for machine translation that uses neural networks to directly map between source and target languages based on the encoder-decoder framework. The chapter closes with a discussion on challenges and future directions of deep learning-based MT.