Try to understand the high-level knowledge behind the timeline/evolution rather than the specifics highlighted below.
Early Machine Translation
- The earliest attempts at machine translation date back to the 1950s.
- These systems relied on rule-based approaches, where linguists manually created dictionaries and grammar rules for translation.
- Rule-Based Machine Translation (RBMT)
- These systems used bilingual dictionaries and syntactic rules to translate text.
- They were limited by the need for extensive manual labor and struggled with idiomatic expressions and complex sentence structures.
Statistical Machine Translation (SMT)
- In the 1990s, Statistical Machine Translation (SMT) emerged as a breakthrough in the field.
- SMT systems used large parallel corpora (collections of texts in multiple languages) to learn translation patterns.
- IBM Model 1
- One of the earliest SMT models, IBM Model 1, used word alignment techniques to map words in the source language to their counterparts in the target language.
- This approach improved translation quality but still struggled with context and fluency.
Phrase-Based SMT
- SMT evolved into phrase-based models, which translated sequences of words (phrases) instead of individual words.
- This approach improved the handling of idiomatic expressions and word order.
- Moses
- An open-source phrase-based SMT system, Moses became widely used in both academia and industry.
- It allowed researchers to experiment with different translation models and contributed to the widespread adoption of SMT.
Neural Machine Translation (NMT)
- The introduction of Neural Machine Translation (NMT) in the mid-2010s marked a significant leap forward.
