Why does a recurrent neural network (RNN) struggle with long-range dependencies in syntax analysis compared to a Transformer network?

💡 Explanation

RNNs struggle because they suffer from the vanishing gradient problem, which hinders their ability to learn relationships over long sequences. Because gradients diminish over many time steps, the network struggles to propagate information effectively; therefore, it cannot accurately model long-range syntactic dependencies, rather than the attention mechanism inherent in Transformers.

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