NLP Machine Translation 5

Question 1

What is the primary purpose of the 'language model' component in a traditional statistical MT system?

Accepted Answer

Assigning probability to target-language word sequences to favor fluent output

Answer

The language model scores candidate translations by their fluency in the target language, preferring grammatically natural word sequences.

Question 2

Which challenge is unique to translating morphologically rich languages (e.g., Finnish, Turkish) in NMT?

Accepted Answer

Data sparsity due to exponentially many word forms

Answer

Morphologically rich languages have vast inflectional paradigms, causing data sparsity since many word forms appear rarely or not at all in training data.

Question 3

In constrained decoding for MT, what is a 'soft constraint' as opposed to a 'hard constraint'?

Accepted Answer

A preference that biases the model toward certain terms without guaranteeing their inclusion

Answer

Soft constraints adjust the probability distribution to favor particular terms but do not guarantee they appear, unlike hard constraints that force specific tokens.

Question 4

What is 'adaptive MT' in a professional translation workflow?

Accepted Answer

A system that updates its model in real time based on translator corrections

Answer

Adaptive MT systems update their parameters on-the-fly as human translators post-edit output, personalizing the model to a specific user or domain during a session.

Question 5

Which property of the transformer makes it more parallelizable during training compared to RNN-based MT models?

Accepted Answer

Self-attention operates on all positions simultaneously rather than sequentially

Answer

Transformer self-attention computes relationships between all token pairs in parallel, whereas RNNs must process tokens one at a time sequentially.

NLP Practice Test

NLP Practice Test

NLP Machine Translation 5