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Incorporating BERT into Parallel Sequence Decoding with Adapters

NeurIPS 2020 · Architectures and efficiency › Attention and transformers · approach: Language model pretraining and induction · 48 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use BERT on both sides with encoder and cross-attention decoder adaptersAB-Net · IWSLT14 De-EnBLEU: 33.59 -> 36.49Helped
Use BERT on both sides with encoder and cross-attention decoder adaptersAB-Net · WMT14 De-EnBLEU: 32.69 -> 33.57Helped
Use parallel AB-Net decoding rather than Transformer-Base decodingWMT14 En-De · WMT14 En-Delatency: 778* ms -> 327 msHelped
Add target-side BERT with decoder cross-attention adapters to the encoder-only variant (ablation)AB-Net-Enc · IWSLT14 De-EnBLEU: 34.45 -> 36.49Helped
Add encoder adapters to Xbert + Decoder (ablation)Xbert + Decoder · IWSLT14 De-EnBLEUHelped
Add decoder adapters to Xbert + Aenc + Ybert (ablation)Xbert + Aenc + Ybert · IWSLT14 De-EnBLEUHelped
Add decoder adapters on all layers rather than only the top 6 layers (ablation)AB-Net · IWSLT14 De-EnBLEU: 34.60 -> 36.49Helped
Add encoder adapters on all layers rather than only the top 6 encoder and decoder layers (ablation)AB-Net · IWSLT14 De-EnBLEU: 33.78 -> 36.49Helped
Halve encoder-adapter hidden dimension from 2^9 to 2^8 (ablation)AB-Net · IWSLT14 De-EnBLEU: 0.4Hurt
Use encoder-adapter hidden dimension d_Aenc=64 (ablation)AB-Net · IWSLT14 De-EnBLEU: 34.81Helped
Fine-tune all AB-Net parameters instead of adapters only (ablation)AB-Net · IWSLT14 De-En validationconvergence speedHurt
Train AB-Net from scratch instead of using pretrained BERT (ablation)AB-Net · IWSLT14 De-En validationconvergenceHurt

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