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The Timeless Relevance of RNN/LSTM: Significance in 2023

4 min readAug 4, 2023

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In recent years, the field of natural language processing (NLP) has witnessed a remarkable shift in the way sequential data is processed and understood. The advent of transformers, particularly the groundbreaking model known as the Transformer architecture, has revolutionized the NLP landscape with its ability to effectively capture long-range dependencies. As a result, many have speculated that recurrent neural networks (RNNs) and their variant, long short-term memory (LSTM), may have been surpassed by these newer and more powerful models. However, despite the rise of transformers, it is crucial to understand why RNN/LSTM remains relevant and continues to play a significant role in the NLP domain even in 2023.

It is still a great baseline

Despite the emergence of transformers as a game-changing model in NLP, the RNN/LSTM architecture still serves as an exceptional baseline for comparison and experimentation.

The following paper presents how simple baselines, such as Linear Classifiers, can still outperform more sophisticated algorithms, like BERT if the latter ones aren’t properly tuned. Using simpler algorithms first, like RNN or Linear classifiers, is a good foundation that could give a sign of the suboptimal performance of more sophisticated algorithms.

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Ching (Chingis)
Ching (Chingis)

Written by Ching (Chingis)

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