Since the RNN’s introduction, ML engineers have made significant progress in natural language processing (NLP) applications with RNNs and their variants. However, the RNN model family has several limitations.
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Exploding gradient
An RNN can wrongly predict the output in the initial training. You need several iterations to adjust the model’s parameters to reduce the error rate. You can describe the sensitivity of the error rate corresponding to the model’s parameter as a gradient. You can imagine a gradient as a slope that you take to descend from a hill. A steeper gradient enables the model to learn faster, and a shallow gradient decreases the learning rate.
Exploding gradient happens when the gradient increases exponentially until the RNN becomes unstable. When gradients become infinitely large, the RNN behaves erratically, resulting in performance issues such as overfitting. Overfitting is a phenomenon where the model can predict accurately with training data but can’t do the same with real-world data.
Vanishing gradient
The vanishing gradient problem is a condition where the model’s gradient approaches zero in training. When the gradient vanishes, the RNN fails to learn effectively from the training data, resulting in underfitting. An underfit model can’t perform well in real-life applications because its weights weren’t adjusted appropriately. RNNs are at risk of vanishing and exploding gradient issues when they process long data sequences.
Slow training time
An RNN processes data sequentially, which limits its ability to process a large number of texts efficiently. For example, an RNN model can analyze a buyer’s sentiment from a couple of sentences. However, it requires massive computing power, memory space, and time to summarize a page of an essay.