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Optimal Completion Distillation for Sequence Learning

ICLR 2019 · Learning theory and algorithms › Combinatorial and search optimization · approach: Constrained neural network optimization · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace MLE with OCDseq2seq · WSJ eval92WER (%): 10.6 -> 9.3Helped
replace MLE with OCDseq2seq · WSJ eval92CER (%): 3.6 -> 3.1Helped
replace scheduled sampling (0.0 -> 0.55) with OCDseq2seq · WSJ eval92WER (%): 10.2 -> 9.3Helped
sample 100% from model with ground-truth log-likelihood (SS 1.0->1.0) instead of OCD (ablation)seq2seq · WSJ eval92CER (%): 3.1 -> 12.1Hurt
fine-tune pretrained SS model with 100% samplingseq2seq SS · WSJ eval92CER (%): 3.6 -> 3.8Hurt
use a single optimal target (shortest) instead of all optimal targets (ablation)OCD · WSJ eval92WER (%): 9.3 -> 12.7Hurt
use a single optimal target (same #words) instead of all optimal targets (ablation)OCD · WSJ eval92WER (%): 9.3 -> 10.2Hurt
use a single optimal target (same #words) instead of all optimal targets (ablation)OCD · WSJ eval92CER (%): 3.1 -> 3.3Hurt
replace MLE with OCDseq2seq · LibriSpeech test-cleanWER (%): 5.7 -> 4.5Helped
replace MLE with OCDseq2seq · LibriSpeech test-otherWER (%): 15.4 -> 13.3Helped
replace MLE with OCDseq2seq · LibriSpeech test-cleanCER (%): 2.9 -> 1.7Helped
replace MLE with OCDseq2seq · LibriSpeech test-otherCER (%): 8.4 -> 6.4Helped

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