Translation CAT Tools and Translation Technology 2

Question 1

What is neural machine translation (NMT) and how does it differ from earlier statistical MT?

Accepted Answer

NMT uses deep learning neural networks to generate more fluent, context-aware translations than statistical phrase-based models

Answer

Neural MT uses large deep learning models to generate translations that are generally more fluent and contextually accurate than older statistical phrase-based systems.

Question 2

What is a 'term extraction' tool used for in translation preparation?

Accepted Answer

Automatically identifying candidate terms in source documents for glossary and termbase creation

Answer

Term extraction tools scan source documents and suggest specialized terminology candidates, helping translators build termbases before translation begins.

Question 3

What does 'leverage' mean in the context of translation project pricing?

Accepted Answer

Using TM matches to reduce the billable word count, since repeated or similar segments require less translation work

Answer

TM leverage reduces the volume of new translation needed by reusing stored segments, which is typically reflected in lower per-word rates for those matches.

Question 4

What is the purpose of an alignment tool in translation?

Accepted Answer

To pair existing source and target documents to build a translation memory from past work

Answer

Alignment tools match source and translated segments in existing document pairs, enabling translators to populate a TM from previously completed work.

Question 5

What is 'quality estimation' (QE) in machine translation?

Accepted Answer

An automated prediction of MT output quality without reference to a human translation

Answer

Quality Estimation uses ML models to predict the quality of MT output without a human reference translation, helping decide how much post-editing is needed.

Translation Practice Test

Translation Practice Test

Translation CAT Tools and Translation Technology 2