AI0-001 Machine Learning and Deep Learning Practice Question
A media company uses a natural language processing (NLP) model to classify news articles into topics. The model was trained on articles from 2015-2018. In 2023, the model's F1 score drops significantly. The data scientists find that the word embeddings no longer capture the meaning of some terms (e.g., 'covid', 'metaverse'). The model uses static word embeddings (Word2Vec) trained on the original corpus. Which solution BEST addresses the observed degradation? A. Replace static embeddings with contextual embeddings from a transformer model like BERT, then fine-tune the classifier. B. Retrain the static Word2Vec embeddings on a larger corpus from 2023. C. Apply data augmentation to the original training data by replacing words with synonyms. D. Increase the dimensionality of the static embeddings.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Replace static embeddings with contextual embeddings from a transformer model like BERT, then fine-tune the classifier.
Contextual embeddings (e.g., BERT) capture meaning based on context, adapting to new uses of words like 'covid' meaning pandemic. Fine-tuning the classifier on new data would update the model. Option A (retraining static embeddings) might capture new word senses but still assigns a single vector per word, missing context. Option B (increasing dimensionality) does not address the semantic shift. Option D (data augmentation) does not introduce new word meanings.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the static Word2Vec embeddings on a larger corpus from 2023.
Why it's wrong here
Static embeddings still lack context dependence; a word like 'covid' would have a single representation, losing nuance.
- ✗
Increase the dimensionality of the static embeddings.
Why it's wrong here
Higher dimensions do not resolve semantic shift; the embeddings still can't distinguish contextual meanings.
- ✓
Replace static embeddings with contextual embeddings from a transformer model like BERT, then fine-tune the classifier.
Why this is correct
Contextual embeddings dynamically represent words based on context, handling semantic shift effectively.
- ✗
Apply data augmentation to the original training data by replacing words with synonyms.
Why it's wrong here
Augmentation does not add new word meanings; it only creates variations of existing data.
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Written by Johnson Ajibi, MSc IT Security
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