What is the reason we prefer LSTM over Vanilla RNN?Question 6Answera.LSTM can easily retain information on the close proximity.b.Good ROUGE Scorec.Vanilla RNN cannot retain the context of the word at beginning of the sentence if the sentence is long which LSTM can do.d.Training time of same number of LSTM node is less.
Question
What is the reason we prefer LSTM over Vanilla RNN?Question 6Answera.LSTM can easily retain information on the close proximity.b.Good ROUGE Scorec.Vanilla RNN cannot retain the context of the word at beginning of the sentence if the sentence is long which LSTM can do.d.Training time of same number of LSTM node is less.
Solution
The main reason we prefer Long Short-Term Memory (LSTM) over Vanilla Recurrent Neural Networks (RNN) is due to the problem of long-term dependencies that RNNs face.
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Long-term dependencies problem: Vanilla RNNs have difficulty carrying information from earlier time steps to later ones. So if a sequence is long and we need to predict the next item in the sequence, RNNs may have a hard time keeping track of information from earlier time steps. This is not the case with LSTMs as they are designed to remember information for long periods of time which makes them more efficient for tasks like text generation, machine translation, etc.
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Vanishing gradient problem: During the training process, RNNs suffer from the vanishing gradient problem where the contribution of information decays geometrically over time which makes it difficult for the RNN to learn and tune the parameters. LSTMs, on the other hand, are immune to the vanishing gradient problem.
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Better performance: LSTMs generally achieve better performance on a variety of tasks. This is because they can remember important information and forget unimportant information due to their gating mechanisms.
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Efficiency: While LSTMs are computationally more intensive than Vanilla RNNs, they are more efficient in terms of their performance and accuracy.
The ROUGE Score is a metric used in text summarization and not directly related to the choice between LSTM and Vanilla RNN. The training time of LSTM nodes being less is also not a general rule, as it can vary based on the complexity of the task, the amount of data, and the computational resources available.
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