Overview
Neural Machine Translation technology uses neural networks (artificial neural networks) to provide accurate translations by considering the meaning and context of the entire sentence.
Terminology
Neural Machine Translation (NMT) uses deep learning technology to generate more natural and fluent translations.
Unlike conventional statistical methods, it processes text by sentence or paragraph. It selects words and phrases with an understanding of the context within a single sentence, making it easier to obtain a coherent translation. However, current technology has limitations in retaining context for long sentences. Recent studies on “context-aware NMT” and “document-level NMT” have been conducted.
Typical examples: DeepL, Google Translate (current version), Microsoft Translator (NMT version), etc.
Use Cases
- For translating business documents, contracts, etc. that require accuracy and naturalness
- For translating complex syntax and long sentences
Benefits of Implementation
- Enables more natural, contextual translations
- Reduces the risk of mistranslations and literal translations
- Allows continuous learning and improvements due to its model structure
Precautions / Challenges
- Requires advanced computational resources for adoption and operation
- Possible issues with translation accuracy for technical terms in certain fields
