Chatbots NLP and AI in Chatbots 2

Question 1

What is 'Named Entity Recognition' (NER) used for in chatbots?

Accepted Answer

Automatically identifying and classifying key information like people, places, and dates in text

Answer

NER allows chatbots to extract structured facts from free-form user input to use in downstream logic.

Question 2

How does a transformer-based model like BERT improve chatbot NLP?

Accepted Answer

By understanding context bidirectionally, capturing word meaning based on surrounding text

Answer

BERT reads text in both directions simultaneously, enabling much deeper contextual understanding than earlier sequential models.

Question 3

What is 'slot filling' in task-oriented chatbots?

Accepted Answer

Collecting all required pieces of information (slots) from the user to complete a specific task

Answer

Slot filling guides the conversation to gather every required parameter before executing an action, like booking a flight.

Question 4

What is 'dialogue state tracking' in a conversational AI system?

Accepted Answer

Maintaining a representation of what has been established in the conversation so far

Answer

Dialogue state tracking keeps a running record of confirmed intents, entities, and context to enable coherent multi-turn conversations.

Question 5

What distinguishes a 'generative' chatbot from a 'retrieval-based' chatbot?

Accepted Answer

Generative bots produce new text responses using language models; retrieval bots select from predefined answers

Answer

Generative models create responses dynamically, offering flexibility, while retrieval-based models pick the best pre-written response from a fixed set.

Chatbots Practice Test

Chatbots Practice Test

Chatbots NLP and AI in Chatbots 2