NLP and AI in Chatbots Flashcards

What is 'sentiment analysis' used for in chatbot platforms?

Answer: Detecting the emotional tone of user messages to adapt bot responses accordingly

Sentiment analysis lets chatbots detect when users are frustrated or satisfied and adjust their tone or escalate to a human agent.

Why is 'data augmentation' important in NLP chatbot training?

Answer: To artificially expand training datasets with paraphrased examples, improving model robustness

Augmenting training data with paraphrases and variations helps the model generalize better across diverse user phrasings.

What is the role of a 'language model' in modern chatbots?

Answer: To predict and generate contextually appropriate text based on learned patterns from large corpora

Language models provide the core ability to understand and generate human-like text that powers modern conversational AI.

What is 'coreference resolution' important for in multi-turn chatbot conversations?

Answer: Identifying that pronouns like 'it' or 'they' refer to previously mentioned entities

Coreference resolution helps the bot correctly interpret pronouns and references that link back to earlier parts of the conversation.

What does 'fine-tuning' a pre-trained language model mean for chatbot development?

Answer: Continuing model training on domain-specific data to specialize its knowledge and style

Fine-tuning adapts a general-purpose language model to a specific domain, improving accuracy on that domain's vocabulary and tasks.

Which evaluation metric measures how well a chatbot's NLP model avoids false intent matches?

Answer: Precision — the proportion of predicted intents that are actually correct

Precision ensures the model doesn't incorrectly assign an intent when the user actually meant something else.