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Beam Search as Approximate Inference Search
Beam search is an example of an approximate search algorithm that keeps only the top K candidates during the search process. Because it is approximate, it is not guaranteed to find the output that maximizes the score.
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Beam Search as Approximate Inference Search
Why can't a scored inference system exhaustively enumerate all possible output sentences to find the highest-scoring one?
True or False: Approximate search algorithms used in scored inference are guaranteed to find the output that maximizes the score function.
Because exhaustively enumerating all possible outputs is infeasible, a scored inference system must apply a(n) _____ algorithm to find a high-scoring output.
Why must a scored inference system use approximate search rather than exhaustively enumerating all possible output sentences?
Approximate search algorithms used in scored inference are guaranteed to find the output S that maximizes Score_A(S).
In a scored inference system, the formal search objective is to compute the _____ of Score_A(S) over all possible output sentences S.
Match each term to its role in a scored inference system for speech recognition.
Order the steps a scored inference system follows to produce a transcription from an audio clip.
What does the quantity P(S|A) represent when used as Score_A(S) in a scored speech recognition system?
For a vocabulary of 50,000 words, there are (50,000)^N possible output sentences of exactly length N.
Approximate search algorithms are _____ to find the output that maximizes the score function, yet they make search computationally tractable.
Match each challenge in scored inference to the concept that best characterizes it.
Order the reasoning steps that build the argument for why approximate search is necessary in scored inference.
Explain the computational challenge and theoretical limitation of search in a scored inference system.
Diagnose search suboptimality in a scored speech recognition system using approximate search.
State the trade-off of using approximate search algorithms in scored inference.
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What does beam search retain at each step of the search process when optimizing a scoring function?
True or False: Beam search is guaranteed to find the output that maximizes the scoring function.
Beam search is an example of an _____ search algorithm that keeps only the top K candidates during the search process.
During beam search, which candidates are retained at each step of the search process?
Beam search is guaranteed to find the output S that maximizes Score_A(S).
Beam search keeps only the top _____ candidates during the search process.
Match each beam search term to its correct description.
Arrange the steps of applying approximate search in a scored inference pipeline.
Why is beam search classified as an 'approximate' search algorithm rather than an exact one?
Beam search is one example of an approximate search algorithm used to optimize a scoring function during inference.
Because beam search is approximate, it is not guaranteed to find the output that _____ Score_A(S).
Match each property of beam search to the consequence it produces.
Order the reasoning steps that explain why beam search may fail to return the globally optimal output.
Explain the relationship between beam search approximations and scoring function optimization during inference.
Diagnosing search limitations when beam search outputs a sub-optimal candidate.
Describe the limitation of using beam search to optimize a scoring function during inference.