NLP Machine Translation 4

Question 1

Which evaluation metric for MT is specifically designed to correlate better with human judgments by incorporating synonyms and paraphrases?

Accepted Answer

METEOR

Answer

METEOR uses stemming, synonym matching, and paraphrase tables to better match human evaluations compared to BLEU's strict n-gram overlap.

Question 2

What is the 'exposure bias' problem in sequence-to-sequence MT training?

Accepted Answer

Training uses ground-truth tokens as input, but inference uses model predictions, causing a distribution mismatch

Answer

Exposure bias arises because teacher-forcing during training always provides correct previous tokens, whereas at test time the model conditions on its own (possibly wrong) outputs.

Question 3

In phrase-based SMT, what does a 'distortion model' control?

Accepted Answer

The reordering (permutation) of translated phrases relative to source order

Answer

The distortion model assigns a cost to jumping between non-adjacent source phrases, controlling how much the target word order can differ from the source.

Question 4

Which post-editing metric measures the minimum number of edit operations needed to correct an MT output into an acceptable translation?

Accepted Answer

TER (Translation Edit Rate)

Answer

TER counts the number of shifts, insertions, deletions, and substitutions needed to convert the MT hypothesis into a reference translation.

Question 5

What is 'pivot translation' and when is it used?

Accepted Answer

Using an intermediate language to translate between two languages that lack parallel data

Answer

Pivot (bridge) translation routes low-resource language pairs through a high-resource pivot language (often English) when direct parallel data is unavailable.

NLP Practice Test

NLP Practice Test

NLP Machine Translation 4