Machine Translation — AI Technical Interview Questions & Answers

Machine Translation

Machine Translation

  • Machine translation maps text from one language to another. Old: phrase tables. Now: encoder–decoder / transformers. Issues: word order, gender, names, low-resource languages. Always evaluate with humans, not only BLEU.
  • Example: Hindi “kal” can be yesterday or tomorrow from context. A model that ignores context looks fluent and wrong.
  • Output: idea / do / check for Machine Translation. Fill those three in the viva.

Technical Interview Tip: Neural MT + one linguistic trap.

Code Examples for Machine Translation

Example

# Machine Translation
print("idea :", "Machine Translation")
print("do   : one tiny example on paper")
print("check: one failure case")

Output: idea / do / check for Machine Translation. Fill those three in the viva.

How to Explain Machine Translation to an Interviewer

Explain Machine Translation as if you are teaching a junior — definition, then one example.

Source → target language. Context / morphology traps.

What does the example print, and what does that prove?

idea / do / check for Machine Translation

What mistake do freshers make with Machine Translation?

Context / morphology traps.