Machine Translation
Machine Translation (MT) is the automatic conversion of text from one language to another while preserving meaning, style, and context using computational methods.
Evolution of MT:
• Rule-based (1950s-1980s): Grammar rules + dictionaries
• Statistical (1990s-2000s): Learn from parallel texts
• Neural (2014+): End-to-end deep learning
• Transformer-based (2017+): Current state-of-the-art
🌍 Impact:
Modern neural MT has made cross-language communication accessible to billions, breaking down language barriers in real-time!
Seq2Seq Architecture
Sequence-to-Sequence Model
Encoder
Input Processing
→
Context Vector
Fixed Representation
→
Decoder
Output Generation
Key Components:
• Encoder: Processes source sentence into fixed representation
• Context Vector: Compressed encoding of input meaning
• Decoder: Generates target sentence word by word
• Attention: Focuses on relevant input parts (later improvement)