Machine Translation (MT) is the task of automatically converting
one natural language into another, preserving the meaning of the
input text, and producing fluent text in the output language. While
machine translation is one of the oldest subfields of artificial
intelligence research, the recent shift towards large-scale empirical
techniques has led to very significant improvements in translation
quality.
The most challenging problems in Natural Language Processing
are
 Pushes the boundaries of NLP
 Involves analysis as well as synthesis
 Involves all layers of NLP: morphology, syntax, semantics,
pragmatics, discourse
 Theory and techniques in MT are applicable to a wide range of
other problems like transliteration, speech recognition and
synthesis
Types for Machine Translation are
1. Example-based MT
2. Rule-based MT
3. Dictionary-based MT
4. Hybrid MT
5. Neural MT
6. Statistical MT
7. Interlingual
8. Transfer-based MT
Applications of MT are
 Google Translator
 Bing Translator
 Systran
 Asia Online