NLP Machine Translation 3

Question 1

Which approach to MT does NOT require any parallel bilingual data during training?

Accepted Answer

Unsupervised MT using monolingual corpora only

Answer

Unsupervised MT methods (e.g., using denoising autoencoders and back-translation on monolingual data) require no parallel sentences.

Question 2

What is the main advantage of using subword tokenization (e.g., BPE) in NMT?

Accepted Answer

Reduces vocabulary size while handling rare and OOV words

Answer

Byte-pair encoding splits rare words into frequent subword units, giving the model coverage of open-vocabulary words without an extremely large vocabulary.

Question 3

In multilingual NMT, what is the 'language token' prepended to the source sentence used for?

Accepted Answer

Signaling the desired target language to a single shared model

Answer

A target-language tag (e.g., <2fr>) prepended to the input tells a universal NMT model which language to generate.

Question 4

Which IBM Model introduced the concept of word fertility in MT alignment?

Accepted Answer

IBM Model 3

Answer

IBM Model 3 introduced fertility, the number of target words that a single source word generates.

Question 5

What does 'zero-shot translation' mean in a multilingual NMT system?

Accepted Answer

Translating between a language pair never seen together in training

Answer

Zero-shot translation is the ability to translate between two languages that were never paired together in training data.

NLP Practice Test

NLP Practice Test

NLP Machine Translation 3