Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions

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Semi-Supervised Sequence Modeling with Cross-View Training (2018, neutral): “(Chiu and Nichols, 2016) 91.62 ± 0.33 86.28 ± 0.26 ID-CNN-CRF (Strubell et al., 2017) 90.65 ± 0.15 86.84 ± 0.19 Tri-Trained LSTM (Lewis et al., 2016) 94.7 Shortcut LSTM (Wu et al., 2017) 95.08 97.53 JMT* (Hashimoto et al., 2017) 95.77 97.55 94.67 92.90 LM-LSTM-CNN-CRF (Liu et al., 2017) 95.96 ± 0.08”
Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “[143] proposed to use dilated convolutions, defined over a wider effective input width by skipping over certain inputs at a time, for better parallelization and context modeling.”
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples (2017, methodology): “, 2006) datasets using the CNN from (Strubell et al., 2017).”
Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, methodology): “[141] proposed to use dilated convolutions, defined over a wider effective input width by skipping over certain inputs at a time, for better”