Natural Language Processing (Artificial Intelligence) 3

Question 1

What is the primary purpose of positional encoding in Transformer models?

Accepted Answer

To inject sequence order information into token embeddings

Answer

Since self-attention is permutation-invariant, positional encodings add order information to token embeddings so the model can distinguish token positions.

Question 2

Which evaluation metric computes the geometric mean of precision and recall of n-gram overlaps between a machine translation output and reference translations?

Accepted Answer

BLEU

Answer

BLEU (Bilingual Evaluation Understudy) computes a modified precision over n-grams and applies a brevity penalty, then takes the geometric mean across n-gram orders.

Question 3

In transformer-based language models, what does 'masked language modeling' (MLM) train the model to do?

Accepted Answer

Predict randomly masked tokens using both left and right context

Answer

MLM, used in BERT, randomly masks a portion of input tokens and trains the model to predict the original tokens using bidirectional context.

Question 4

What is 'transfer learning' in the context of NLP?

Accepted Answer

Using knowledge from a pre-trained model to improve performance on a new task

Answer

Transfer learning leverages representations learned during pre-training on large corpora and adapts them to downstream tasks, requiring far less task-specific data.

Question 5

Which of the following best describes the difference between stemming and lemmatization?

Accepted Answer

Lemmatization returns the dictionary base form; stemming chops characters heuristically

Answer

Lemmatization uses vocabulary and morphological analysis to return the canonical base form, while stemming applies crude suffix-stripping rules that may not yield real words.

Natural Language Processing Practice Test

Natural Language Processing Practice Test

Natural Language Processing (Artificial Intelligence) 3