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6.1 Machine Translation |
On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) |
in Table 2) outperforms the best previously reported models (including ensembles) by more than 2.0 |
BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is |
listed in the bottom line of Table 3. Training took 3.5 days on 8 P100 GPUs. Even our base model |
surpasses all previously published models and ensembles, at a fraction of the training cost of any of |
the competitive models. |
On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, |
outperforming all of the previously published single models, at less than 1/4 the training cost of the |
previous state-of-the-art model. The Transformer (big) model trained for English-to-French used |
dropout rate Pdrop = 0.1, instead of 0.3. |
For the base models, we used a single model obtained by averaging the last 5 checkpoints, which |
were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We |
used beam search with a beam size of 4 and length penalty α = 0.6 [38]. These hyperparameters |
were chosen after experimentation on the development set. We set the maximum output length during |
inference to input length + 50, but terminate early when possible [38]. |
Table 2 summarizes our results and compares our translation quality and training costs to other model |
architectures from the literature. We estimate the number of floating point operations used to train a |
model by multiplying the training time, the number of GPUs used, and an estimate of the sustained |
single-precision floating-point capacity of each GPU 5 |
. |
6.2 Model Variations |
To evaluate the importance of different components of the Transformer, we varied our base model |
in different ways, measuring the change in performance on English-to-German translation on the |
development set, newstest2013. We used beam search as described in the previous section, but no |
checkpoint averaging. We present these results in Table 3. |
In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, |
keeping the amount of computation constant, as described in Section 3.2.2. While single-head |
attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads. |
5We used values of 2.8, 3.7, 6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively. |
8 |
Table 3: Variations on the Transformer architecture. Unlisted values are identical to those of the base |
model. All metrics are on the English-to-German translation development set, newstest2013. Listed |
perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to |
per-word perplexities. |
N dmodel dff h dk dv Pdrop ls |
train PPL BLEU params |
steps (dev) (dev) ×106 |
base 6 512 2048 8 64 64 0.1 0.1 100K 4.92 25.8 65 |
(A) |
1 512 512 5.29 24.9 |
4 128 128 5.00 25.5 |
16 32 32 4.91 25.8 |
32 16 16 5.01 25.4 |
(B) 16 5.16 25.1 58 |
32 5.01 25.4 60 |
(C) |
2 6.11 23.7 36 |
4 5.19 25.3 50 |
8 4.88 25.5 80 |
256 32 32 5.75 24.5 28 |
1024 128 128 4.66 26.0 168 |
1024 5.12 25.4 53 |
4096 4.75 26.2 90 |
(D) |
0.0 5.77 24.6 |
0.2 4.95 25.5 |
0.0 4.67 25.3 |
0.2 5.47 25.7 |
(E) positional embedding instead of sinusoids 4.92 25.7 |
big 6 1024 4096 16 0.3 300K 4.33 26.4 213 |
Table 4: The Transformer generalizes well to English constituency parsing (Results are on Section 23 |
of WSJ) |
Parser Training WSJ 23 F1 |
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative 88.3 |
Petrov et al. (2006) [29] WSJ only, discriminative 90.4 |
Zhu et al. (2013) [40] WSJ only, discriminative 90.4 |
Dyer et al. (2016) [8] WSJ only, discriminative 91.7 |
Transformer (4 layers) WSJ only, discriminative 91.3 |
Zhu et al. (2013) [40] semi-supervised 91.3 |
Huang & Harper (2009) [14] semi-supervised 91.3 |
McClosky et al. (2006) [26] semi-supervised 92.1 |
Vinyals & Kaiser el al. (2014) [37] semi-supervised 92.1 |
Transformer (4 layers) semi-supervised 92.7 |
Luong et al. (2015) [23] multi-task 93.0 |
Dyer et al. (2016) [8] generative 93.3 |
In Table 3 rows (B), we observe that reducing the attention key size dk hurts model quality. This |
suggests that determining compatibility is not easy and that a more sophisticated compatibility |
function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, |
bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our |
sinusoidal positional encoding with learned positional embeddings [9], and observe nearly identical |
results to the base model. |
6.3 English Constituency Parsing |
To evaluate if the Transformer can generalize to other tasks we performed experiments on English |
constituency parsing. This task presents specific challenges: the output is subject to strong structural |
9 |
constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence |
models have not been able to attain state-of-the-art results in small-data regimes [37]. |
We trained a 4-layer transformer with dmodel = 1024 on the Wall Street Journal (WSJ) portion of the |
Penn Treebank [25], about 40K training sentences. We also trained it in a semi-supervised setting, |
using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences |
[37]. We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens |
for the semi-supervised setting. |
We performed only a small number of experiments to select the dropout, both attention and residual |
(section 5.4), learning rates and beam size on the Section 22 development set, all other parameters |
remained unchanged from the English-to-German base translation model. During inference, we |
increased the maximum output length to input length + 300. We used a beam size of 21 and α = 0.3 |
for both WSJ only and the semi-supervised setting. |
Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the |
Recurrent Neural Network Grammar [8]. |
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