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A Continuous Relaxation of Beam Search for End-to-End Training of Neural Sequence Models

AAAI 2018 · Architectures and efficiency › RNNs and normalization · approach: Associative memory networks · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train with continuous beam direct loss and anneal αSeq2seq · CCG Supertagging testtag-level accuracy: 80.35 -> 85.82Helped
Train with continuous beam direct loss and anneal αSeq2seq · NER testmacro F1: 54.92 -> 56.38Helped
Use hard beam search instead of greedy decoding with a CE-trained modelSeq2seq · CCG Supertagging testtag-level accuracy: 80.35 -> 82.42Helped
Use hard beam search instead of greedy decoding with a CE-trained modelSeq2seq · NER testmacro F1: 54.92 -> 51.34Hurt
Use annealed α instead of constant α=1.0 for direct-loss training (ablation)Continuous beam direct loss · CCG Supertagging testtag-level accuracy: 82.65 -> 85.82Helped
Use annealed α instead of constant α=1.0 for direct-loss training (ablation)Continuous beam direct loss · NER testmacro F1: 54.08 -> 56.15Helped
Use annealed α instead of constant α=1.0 for approximate hinge training (ablation)Approximate hinge · CCG Supertagging testtag-level accuracy: 83.36 -> 83.54Helped
Use annealed α instead of constant α=1.0 for approximate hinge training (ablation)Approximate hinge · NER testmacro F1: 44.67 -> 45.98Helped
Train with approximate hinge loss rather than continuous beam direct lossSeq2seq · CCG Supertagging testtag-level accuracy: 85.82 -> 83.54Hurt
Train with approximate hinge loss rather than continuous beam direct lossSeq2seq · NER testmacro F1: 56.15 -> 45.98Hurt
Use soft beam decoding instead of hard beam decoding after direct-loss trainingContinuous beam direct loss with annealed α · CCG Supertagging testtag-level accuracy: 85.82 -> 85.78No clear effect
Use soft beam decoding instead of hard beam decoding after direct-loss trainingContinuous beam direct loss with annealed α · NER testmacro F1: 56.15 -> 56.38No clear effect

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