Overview
Statistical Machine Translation is a technology that uses large amounts of bilingual data to build statistical models and automatically generate translations based on probabilistic relationships.
Terminology
Statistical Machine Translation (SMT) uses statistical methods to learn translation patterns based on bilingual corpora (document data) in the source and target languages to translate new texts.
It makes probabilistic judgements on a word or phrase basis to generate the best translation.
Typical examples: Google Translate (early version), Microsoft Translator (early version), etc.
Use Cases
- When you need to quickly translate a large number of documents
- When you need a quick overview of internal documents, web pages, etc.
Benefits of Implementation
- Possible to translate a large number of documents in a short period of time
- Reduced translation costs compared to human translation
- Useful for grasping the overall content in the early stages of translation work
Precautions / Challenges
- Difficult to accurately convey context and nuance, may provide translations of uneven quality due to the nature of a method that simply combines the most probable phrases without considering context. This may also result in unnatural-sounding translations
- Lower precision for text that contains many technical terms and unique expressions
