Neural Machine Translation: A Review of Methods, Resources, and Tools
This paper provides a comprehensive review for researchers and practitioners in the field of natural language processing, specifically those working on machine translation.
This paper reviews methods, resources, and tools for Neural Machine Translation (NMT), a sub-field of natural language processing that translates natural languages using computers. It focuses on architectures, decoding, and data augmentation methods, and summarizes useful resources and tools for researchers.
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machine translation (NMT) has achieved great success and has become the new mainstream method in practical MT systems. In this article, we first provide a broad review of the methods for NMT and focus on methods relating to architectures, decoding, and data augmentation. Then we summarize the resources and tools that are useful for researchers. Finally, we conclude with a discussion of possible future research directions.