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Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models

ICML 2019 · Architectures and efficiency › Attention and transformers · approach: Language model pretraining and induction · 42 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Increase beam width from 5 to 250 without constraintsbeam search decoding of convolutional seq2seq · WMT'14 En-DeBLEU4: 26.11 to 21.38Hurt
Apply discrepancy gap constraint (M=1.5) to beam searchbeam search decoding of convolutional seq2seq · WMT'14 En-DeBLEU4: 21.38 to 26.29 at B=250Helped
Apply beam candidate rank constraint (N=2) to beam searchbeam search decoding of convolutional seq2seq · WMT'14 En-DeBLEU4: 21.38 to 26.10 at B=250Helped
Increase beam width from 5 to 250 without constraintsbeam search decoding of convolutional seq2seq · WMT'14 En-FrBLEU4: 40.83 to 35.03Hurt
Apply discrepancy gap constraint (M=2.0) to beam searchbeam search decoding of convolutional seq2seq · WMT'14 En-FrBLEU4: 35.03 to 41.06 at B=250Helped
Increase beam width from 3 to 250 without constraintsbeam search decoding of summarization model · GigawordR-1 F: 34.22 to 33.23Hurt
Apply discrepancy gap constraint (M=0.85) to beam searchsummarization beam search · GigawordR-1 F: 33.23 to 34.32 at B=250Helped
Increase beam width from 3 to 250 without constraintsbeam search decoding of image captioning model · MSCOCOBLEU4: 32.36 to 29.79Hurt
Apply discrepancy gap constraint (M=0.45) to beam searchimage captioning beam search · MSCOCOBLEU4: 29.79 to 32.35 at B=250Helped
Apply beam candidate rank constraint (N=2) to beam searchimage captioning beam search · MSCOCOBLEU4: 29.79 to 30.87 at B=250; gap constraint is better hereHelped
Length normalization of hypothesis log-likelihoodWMT'14 En-De beam search · WMT'14 En-DeBLEU4No clear effect

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