AWS Certified Machine Learning – Specialty — Question 74

A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

Answer options

• A. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
• B. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
• C. Reduce the learning rate and run the training process until the training loss stops decreasing.
• D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

Correct answer: D

Explanation

The correct answer is D because using word2vec embeddings that are pretrained allows the model to leverage existing semantic relationships in the data, which can significantly enhance performance. Options A and C involve different initialization or learning rate adjustments that may not provide as substantial a boost as using pretrained embeddings. Option B suggests switching to GRUs, which may not necessarily improve performance over LSTMs in this context.