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Why are Bidirectional LSTMs particularly effective for tasks like speech recognition and text analysis?

easy📝 Conceptual Q2 of 15
NLP - Sequence Models for NLP
Why are Bidirectional LSTMs particularly effective for tasks like speech recognition and text analysis?
ABecause they capture context from both past and future tokens in a sequence.
BBecause they reduce the number of parameters compared to unidirectional LSTMs.
CBecause they only require half the training data.
DBecause they use convolutional filters to extract features.
Step-by-Step Solution
Solution:

  1. Step 1: Identify the importance of context

    Many NLP tasks require understanding both previous and upcoming words.

  2. Step 2: Role of Bidirectional LSTM

    Bidirectional LSTMs process sequences forward and backward, capturing full context.

  3. Final Answer:

    Because they capture context from both past and future tokens in a sequence. -> Option A

  4. Quick Check:

    Bidirectional means two-way context [OK]
Quick Trick: Bidirectional captures past and future context [OK]
Common Mistakes:
MISTAKES
  • Assuming fewer parameters than unidirectional LSTM
  • Confusing with convolutional or data requirements

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