A company uses an LSTM to label each sentence in multi-page documents as “risk” or “no risk.” Despite trying many network structures and hyperparameter settings, performance is poor. Which change will likely give the LARGEST performance improvement?

Initializing word inputs with pretrained word2vec embeddings trained on a relevant corpus (energy-sector news articles) is likely to provide the largest performance improvement. Word2vec captures semantic relationships between words, allowing the model to understand context and meaning, which is crucial for tasks like risk assessment in text. Pretraining on a large, relevant corpus ensures these embeddings are rich and domain-specific, providing a strong foundation for the LSTM. TF-IDF vectors, while useful for keyword extraction, do not capture semantic relationships as effectively as word embeddings, making them less suitable for complex text classification. Replacing LSTMs with GRUs might offer minor improvements in efficiency or convergence but won't fundamentally address a lack of rich word representations. Lowering the learning rate and continuing training is a fine-tuning step, not a foundational change that would yield a "largest" improvement if the initial representations are poor.