Artificial Intelligence Natural Language Processing 2

Question 1

What architecture is BERT (Bidirectional Encoder Representations from Transformers) based on?

Accepted Answer

Transformer encoder

Answer

BERT uses only the encoder portion of the Transformer and is pre-trained with bidirectional context, reading the entire sentence at once.

Question 2

What does 'fine-tuning' a pre-trained language model involve?

Accepted Answer

Continuing training on a task-specific labeled dataset to adapt the model to a new task

Answer

Fine-tuning continues gradient-based training of a pre-trained model on a smaller task-specific dataset, adapting general representations to the target task.

Question 3

What is the primary difference between extractive and abstractive text summarization?

Accepted Answer

Extractive selects existing sentences from the source; abstractive generates new sentences

Answer

Extractive summarization picks and ranks existing sentences from the document, while abstractive summarization generates novel sentences that may not appear verbatim in the source.

Question 4

What is a 'language model' in NLP?

Accepted Answer

A model that assigns probabilities to sequences of words or predicts the next word in a sequence

Answer

A language model learns the probability distribution over sequences of words, enabling tasks like text generation, completion, and scoring sentence fluency.

Question 5

Which NLP task determines whether the relationship between two sentences is entailment, contradiction, or neutral?

Accepted Answer

Natural language inference

Answer

Natural language inference (NLI) classifies the logical relationship between a premise and a hypothesis sentence as entailment, contradiction, or neutral.

Artificial Intelligence Practice Test

Artificial Intelligence Practice Test

Artificial Intelligence Natural Language Processing 2