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Why are LSTM networks more effective than vanilla RNNs when processing long text sequences?

easy📝 Conceptual Q1 of 15
NLP - Sequence Models for NLP
Why are LSTM networks more effective than vanilla RNNs when processing long text sequences?
ABecause LSTMs can better capture long-term dependencies by mitigating the vanishing gradient problem
BBecause LSTMs require less computational power than vanilla RNNs
CBecause LSTMs use convolutional layers internally for feature extraction
DBecause LSTMs do not use any gating mechanisms
Step-by-Step Solution
Solution:

  1. Step 1: Understand RNN limitations

    Vanilla RNNs struggle with long-term dependencies due to vanishing gradients.

  2. Step 2: Role of LSTM gates

    LSTMs use gates (input, forget, output) to control information flow and preserve gradients.

  3. Final Answer:

    Because LSTMs can better capture long-term dependencies by mitigating the vanishing gradient problem -> Option A

  4. Quick Check:

    Long-term dependency handling [OK]
Quick Trick: LSTMs solve vanishing gradients with gating [OK]
Common Mistakes:
MISTAKES
  • Assuming LSTMs are computationally cheaper
  • Confusing LSTMs with convolutional networks
  • Ignoring the gating mechanism

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