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Decoding with Value Networks for Neural Machine Translation

NeurIPS 2017 · Architectures and efficiency › RNNs and normalization · approach: Non-autoregressive translation · 49 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add value network reranking during beam search (NMT-VNN) vs classic beam searchRNN NMT · En→Fr newstest2014BLEU: +1.03Helped
add value network reranking during beam search (NMT-VNN) vs classic beam searchRNN NMT · En→De newstest2014BLEU: +1.3Helped
value network decoding instead of beam search optimization (NMT-BSO)RNN NMT · En→Fr newstest2014BLEU: +0.31Helped
value network decoding instead of beam search optimization (NMT-BSO)RNN NMT · En→De newstest2014BLEU: +0.33Helped
add value network reranking during beam search vs classic beam searchRNN NMT · Zh→En NIST06BLEU: +1.4Helped
add value network reranking during beam search vs classic beam searchRNN NMT · Zh→En NIST08BLEU: +1.82Helped
value network decoding instead of beam search optimization (NMT-BSO)RNN NMT · Zh→En NIST06BLEU: +1.01Helped
value network decoding instead of beam search optimization (NMT-BSO)RNN NMT · Zh→En NIST08BLEU: +0.72Helped
add value network decoding to a deep 4-layer LSTM NMT modeldeep 4-layer LSTM NMT · En→FrBLEU: +0.33Helped
increase beam size with value network (NMT-VNN) vs without (ablation)RNN NMT · En→De beam size sweepBLEUHelped
value network decoding hyperparameter alpha varied 0.7 to 0.95 (ablation)NMT-VNN · En→DeBLEUNo clear effect

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