Slot Filling and Intent Detection Quiz

Answers and explanations appear as you take the quiz.

1. What is the difference between intent detection and slot filling?
   • Intent finds the overall goal; slots extract specific detail values
   • They are two names for the same task
   • Intent extracts values; slots classify the goal
   • Slots handle audio and intent handles text
2. In 'Book a flight from Boston to Denver,' what is the intent?
   • Boston
   • book_flight
   • Denver
   • destination
3. Why is slot filling often framed as sequence labeling with BIO tags?
   • To count the number of words
   • To translate the sentence first
   • To capture multi-word slot values correctly
   • To detect the speaker's emotion
4. Why do modern systems train intent detection and slot filling jointly?
   • It is required by law
   • Joint training uses less electricity
   • Slots cannot be computed otherwise
   • The tasks are tightly coupled and share useful signal
5. What role can a CRF layer play on top of slot tags?
   • It enforces valid label sequences between tokens
   • It detects the intent label
   • It translates the utterance
   • It compresses the model size
6. How can a large language model produce an intent-and-slot result?
   • By eliminating user input
   • By generating structured output such as schema-constrained JSON, with validation still required
   • By using only single-word commands
   • By removing the concepts of intent and slot
7. What domain does the ATIS benchmark focus on?
   • Music source separation
   • Image inpainting
   • Airline travel information and flight requests
   • Robot navigation
8. In a multi-turn dialogue, why might a slot-filling system need prior turns?
   • Earlier turns remove the need to identify an intent
   • Slot values can only appear in the first sentence
   • Prior context changes the tokenizer vocabulary
   • A follow-up may omit a value type that earlier context supplies