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Attention Is All You Need

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Empirical Results and Performance

Machine Translation Performance and Training Costs

The Transformer model achieves state-of-the-art BLEU scores on both English-to-German (EN-DE) and English-to-French (EN-FR) translation tasks while requiring significantly lower training costs compared to previous recurrent or convolutional architectures.

Sources

S6.T2

Table 2: The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost. Model BLEU Training Cost (FLOPs) EN-DE EN-FR EN-DE EN-FR ByteNet [18] 23.75 Deep-Att + PosUnk [39] 39.2 1.0⋅10201.0\cdot 10^{20} GNMT + RL [38] 24.6 39.92 2.3⋅10192.3\cdot 10^{19} 1.4⋅10201.4\cdot 10^{20} ConvS2S [9] 25.16 40.46 9.6⋅10189.6\cdot 10^{18} 1.5⋅10201.5\cdot 10^{20} MoE [32] 26.03 40.56 2.0⋅10192.0\cdot 10^{19} 1.2⋅10201.2\cdot 10^{20} Deep-Att + PosUnk Ensemble [39] 40.4 8.0⋅10208.0\cdot 10^{20} GNMT + RL Ensemble [38] 26.30 41.16 1.8⋅10201.8\cdot 10^{20} 1.1⋅10211.1\cdot 10^{21} ConvS2S Ensemble [9] 26.36 41.29 7.7⋅10197.7\cdot 10^{19} 1.2⋅10211.2\cdot 10^{21} Transformer (base model) 27.3 38.1 3.3⋅𝟏𝟎𝟏𝟖3.3\cdot 10^{18} Transformer (big) 28.4 41.8 2.3⋅10192.3\cdot 10^{19}

Reported values
Measure	Value

Sources

S6.T2

Table 2: The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost. Model BLEU Training Cost (FLOPs) EN-DE EN-FR EN-DE EN-FR ByteNet [18] 23.75 Deep-Att + PosUnk [39] 39.2 1.0⋅10201.0\cdot 10^{20} GNMT + RL [38] 24.6 39.92 2.3⋅10192.3\cdot 10^{19} 1.4⋅10201.4\cdot 10^{20} ConvS2S [9] 25.16 40.46 9.6⋅10189.6\cdot 10^{18} 1.5⋅10201.5\cdot 10^{20} MoE [32] 26.03 40.56 2.0⋅10192.0\cdot 10^{19} 1.2⋅10201.2\cdot 10^{20} Deep-Att + PosUnk Ensemble [39] 40.4 8.0⋅10208.0\cdot 10^{20} GNMT + RL Ensemble [38] 26.30 41.16 1.8⋅10201.8\cdot 10^{20} 1.1⋅10211.1\cdot 10^{21} ConvS2S Ensemble [9] 26.36 41.29 7.7⋅10197.7\cdot 10^{19} 1.2⋅10211.2\cdot 10^{21} Transformer (base model) 27.3 38.1 3.3⋅𝟏𝟎𝟏𝟖3.3\cdot 10^{18} Transformer (big) 28.4 41.8 2.3⋅10192.3\cdot 10^{19}