NLP vs NLU vs NLG - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the difference between NLP, NLU, and NLG

Which of the following best describes Natural Language Understanding (NLU)?

A Analyzing and interpreting the meaning of human language

B Generating human-like text based on input data

C Converting speech to text

D Translating text from one language to another

💡 Hint

Think about which process involves understanding the meaning behind words.

NLU focuses on interpreting and extracting meaning from human language, unlike NLG which is about generating text.

🧠 Conceptual

intermediate

Identifying Natural Language Generation (NLG)

Which task is an example of Natural Language Generation (NLG)?

A Summarizing a long article into a short paragraph

B Detecting the sentiment of a tweet

C Transcribing audio recordings into text

D Parsing sentences to identify parts of speech

💡 Hint

Consider which option involves creating new text from data.

NLG is about producing human-like text, such as summaries or reports, from data or other text.

🧠 Conceptual

advanced

Role of NLP in AI systems

Which statement correctly explains the relationship between NLP, NLU, and NLG?

A NLG is the process of understanding language, while NLU generates text

B NLP is a subset of NLU and NLG

C NLU and NLG are independent fields unrelated to NLP

D NLP is the broad field that includes both NLU and NLG as subfields

💡 Hint

Think about which is the broadest term covering the others.

NLP is the overall field of working with human language, and it includes NLU (understanding) and NLG (generation) as parts.

❓ Model Choice

advanced

Choosing the right model for NLU task

You want to build a system that understands customer complaints and extracts the main issues. Which type of model is best suited for this NLU task?

A A text generation model like GPT to write responses

B A named entity recognition model to identify key information

C A sentiment analysis model to classify emotions

D A speech-to-text model to convert audio complaints

💡 Hint

Focus on extracting important details from text.

Named entity recognition helps identify key pieces of information, which is essential for understanding complaints.

❓ Metrics

expert

Evaluating NLG model quality

You have trained an NLG model to generate product descriptions. Which metric is most appropriate to evaluate how well the generated text matches human-written descriptions?

A Confusion matrix of predicted vs actual labels

B Accuracy of classifying text into categories

C BLEU score measuring overlap of generated and reference text

D Mean squared error between predicted and actual numeric values

💡 Hint

Think about metrics that compare generated text to reference text.

BLEU score is commonly used to evaluate the quality of generated text by comparing it to human references.