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Agreement on Target-Bidirectional LSTMs for Sequence-to-Sequence Learning

AAAI 2016 · Architectures and efficiency › RNNs and normalization · approach: LSTM design and memory · 37 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add right-to-left LSTM with agreement decoding to l2r LSTMGLSTM · GM-PMWER: 29.6 -> 23.8Helped
add right-to-left LSTM with agreement decoding to l2r LSTMGLSTM · JP-ENACC: 28.3 -> 33.3Helped
add right-to-left LSTM with agreement decoding to l2r LSTMGLSTM · EN-JPACC: 40.1 -> 43.8Helped
use bidirectional agreement ensembles instead of same-direction ensemble of 5 LSTMsELSTM · JP-ENACC: 34.2 -> 36.3Helped
use bidirectional agreement ensembles instead of same-direction ensemble of 5 LSTMsELSTM · GM-PMWER: 22.1 -> 21.2Helped
use polynomial approximation instead of joint k-best search (ablation)BLSTM · JP-ENACC: 33.3 -> 33.4No clear effect
rescore joint k-best of both directions instead of l2r k-best only (ablation)BLSTM · JP-ENACC: 32.7 -> 33.3Helped
use BLSTM instead of 2-LSTM same-direction ensemble (ablation)bidirectional agreement · JP-ENACC: 31.2 -> 33.3Helped
increase ensemble from 5+5 to 10+10 directional LSTMsBELSTM · JP-ENACC: 36.3 -> 36.5Helped
use BELSTM on long sequences vs ELSTM(5r2l)BELSTM · JP-EN long sequencesACC: 28.6 -> 28.6No clear effect
add bidirectional agreement to l2r LSTM on long sequencesGLSTM(l2r) · JP-EN long sequencesACC: 17.3 -> 25.0Helped
add bidirectional agreement to improve suffix qualityGLSTM(l2r) · JP-EN long sequencessuffix precision: 65% -> 74%Helped

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