NLP - Text Generation
You want to build a trigram language model to predict the next word given two previous words. Which approach best handles the problem of unseen trigrams in your training data?
You want to build a trigram language model to predict the next word given two previous words. Which approach best handles the problem of unseen trigrams in your training data?
hard📝 Model Choice Q15 of 15
Step-by-Step Solution
Solution:
Step 1: Understand the unseen trigram problem
Unseen trigrams cause zero probabilities, which harm model predictions.Step 2: Identify solution to zero probability issue
Smoothing techniques like Kneser-Ney adjust probabilities to handle unseen cases effectively.Step 3: Evaluate other options
Ignoring unseen trigrams or only using unigram probabilities lose context; increasing data alone may not solve sparsity.Final Answer:
Use smoothing techniques like Kneser-Ney smoothing -> Option CQuick Check:
Smoothing fixes zero probs for unseen trigrams [OK]
Quick Trick: Use smoothing to avoid zero probabilities [OK]
Common Mistakes:
MISTAKES- Assigning zero probability to unseen trigrams
- Ignoring context by using only unigrams
- Relying solely on more data without smoothing
Master "Text Generation" in NLP
Want More Practice?
15+ quiz questions · All difficulty levels · Free
Free Signup - Practice All QuestionsMore NLP Quizzes