- Basic models
- Beam search
- Refinements to beam search
- Error analysis on beam search
Human: Jane visite Africa in September.(y* )
Algorithm: Jane visited Africa last September.(y^)
Case 1: P(y* |x) > p(y^|x)
Beam search chose y^. But y* attains higerP(y|x). Conclusion: Beam search is at fault.
Case 2: P(y* |x) <= P(y^|x)
y* is a better translation than Y^. But RNN predictedP(y* |x)<P(y^|x).
Conclusion: RNN model is at fault.
Through much of up process, figures out what faction of errors are “due to” beam search vs. RNN model.(BRBRBBBBRRR which one is the most)
- Bleu score
- Attention model intuition
- Attention model
attention model for human readable data to compute readable data, a simple version(reduce yt-1 as a input to LSTM):
visualize the a< t,t’>
- Speech recognition
x(audio clip)—————–>y(transcript)”the quick brown fox”
- Trigger word detection
Trigger word detection algorithm
Trigger word model