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संबंधित मार्गदर्शक पथद्विदिश आवर्ती नेटवर्क
प्रश्न 1 का 8

What does a bidirectional RNN do with an input sequence?

प्रत्येक प्रश्न का पूर्वावलोकन करें8 प्रश्न

जैसे ही आप प्रश्नोत्तरी लेते हैं उत्तर और स्पष्टीकरण दिखाई देते हैं।

  1. What does a bidirectional RNN do with an input sequence?
    • Processes only the first half forward
    • Processes it in forward and backward directions and combines the contextual states
    • Uses future tokens as training labels
    • Runs the same hidden state twice in the same direction
  2. Why can context from both directions help label a word such as “bank”?
    • Words before and after it can clarify the intended meaning
    • The word has only one possible meaning
    • The model needs no surrounding text
    • The label is stored in the sequence position
  3. How are the two directional hidden states commonly combined at a position?
    • Average all positions in the sequence first
    • Discard the backward state
    • Use only the final forward state for every position
    • Concatenate them or otherwise combine them for the output
  4. Why can a bidirectional RNN be unsuitable for streaming generation?
    • It cannot process tokens in order
    • It requires class labels at inference
    • It needs future sequence context that is not yet available
    • यह केवल छवियों के साथ काम करता है
  5. What is one advantage of offline sequence labeling with bidirectionality?
    • Each position can use context on both sides
    • The model needs no sequence boundaries
    • It removes the need for recurrent parameters
    • It guarantees correct token labels
  6. Do forward and backward passes share the same recurrent parameters in the guide’s described architecture?
    • Yes, by definition every bidirectional RNN shares weights
    • No; it uses separate recurrent layers for the two directions
    • Only when the sequence has even length
    • They have no parameters
  7. Which task is a natural fit when the entire sequence is available?
    • Left-to-right text generation before later tokens exist
    • Live word prediction with zero lookahead
    • Part-of-speech tagging or named-entity recognition
    • Predicting the next unseen sensor reading in a stream
  8. What does bidirectional context not guarantee?
    • Access to following input tokens in offline use
    • A combined representation at each position
    • Two traversal directions
    • That the model will correctly interpret every token