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Soru 1 arasında 8
What does a bidirectional RNN do with an input sequence?
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Testi çözdüğünüzde cevaplar ve açıklamalar görünür.
- 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
- 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
- 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
- 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
- Yalnızca görsellerle çalışır
- 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
- 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
- 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
- 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