puffy310/yandset · Datasets at Hugging Face

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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].

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].