Provided proper attribution is provided, Google hereby grants permission to

reproduce the tables and ﬁgures in this paper solely for use in journalistic or

scholarly works.

Attention Is All You Need

Ashish Vaswani

∗

Google Brain

avaswani@google.com

Noam Shazeer

∗

Google Brain

noam@google.com

Niki Parmar

∗

Google Research

nikip@google.com

Jakob Uszkoreit

∗

Google Research

usz@google.com

Llion Jones

∗

Google Research

llion@google.com

Aidan N. Gomez

∗ †

University of Toronto

aidan@cs.toronto.edu

Łukasz Kaiser

∗

Google Brain

lukaszkaiser@google.com

Illia Polosukhin

∗ ‡

illia.polosukhin@gmail.com

Abstract

The dominant sequence transduction models are based on complex recurrent or

convolutional neural networks that include an encoder and a decoder. The best

performing models also connect the encoder and decoder through an attention

mechanism. We propose a new simple network architecture, the Transformer,

based solely on attention mechanisms, dispensing with recurrence and convolutions

entirely. Experiments on two machine translation tasks show these models to

be superior in quality while being more parallelizable and requiring signiﬁcantly

less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-

to-German translation task, improving over the existing best results, including

ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task,

our model establishes a new single-model state-of-the-art BLEU score of 41.8 after

training for 3.5 days on eight GPUs, a small fraction of the training costs of the

best models from the literature. We show that the Transformer generalizes well to

other tasks by applying it successfully to English constituency parsing both with

large and limited training data.

∗

Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started

the effort to evaluate this idea. Ashish, with Illia, designed and implemented the ﬁrst Transformer models and

has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head

attention and the parameter-free position representation and became the other person involved in nearly every

detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and

tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and

efﬁcient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and

implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating

our research.

†

Work performed while at Google Brain.

‡

Work performed while at Google Research.

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

arXiv:1706.03762v7 [cs.CL] 2 Aug 2023

1 Introduction

Recurrent neural networks, long short-term memory [

] and gated recurrent [

] neural networks

in particular, have been ﬁrmly established as state of the art approaches in sequence modeling and

transduction problems such as language modeling and machine translation [

]. Numerous

efforts have since continued to push the boundaries of recurrent language models and encoder-decoder

architectures [38, 24, 15].

Recurrent models typically factor computation along the symbol positions of the input and output

sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden

states

, as a function of the previous hidden state

t−1

and the input for position

. This inherently

sequential nature precludes parallelization within training examples, which becomes critical at longer

sequence lengths, as memory constraints limit batching across examples. Recent work has achieved

signiﬁcant improvements in computational efﬁciency through factorization tricks [

] and conditional

computation [

], while also improving model performance in case of the latter. The fundamental

constraint of sequential computation, however, remains.

Attention mechanisms have become an integral part of compelling sequence modeling and transduc-

tion models in various tasks, allowing modeling of dependencies without regard to their distance in

the input or output sequences [

]. In all but a few cases [

], however, such attention mechanisms

are used in conjunction with a recurrent network.

In this work we propose the Transformer, a model architecture eschewing recurrence and instead

relying entirely on an attention mechanism to draw global dependencies between input and output.

The Transformer allows for signiﬁcantly more parallelization and can reach a new state of the art in

translation quality after being trained for as little as twelve hours on eight P100 GPUs.

2 Background

The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU

[

], ByteNet [

] and ConvS2S [

], all of which use convolutional neural networks as basic building

block, computing hidden representations in parallel for all input and output positions. In these models,

the number of operations required to relate signals from two arbitrary input or output positions grows

in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes

it more difﬁcult to learn dependencies between distant positions [

]. In the Transformer this is

reduced to a constant number of operations, albeit at the cost of reduced effective resolution due

to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as

described in section 3.2.

Self-attention, sometimes called intra-attention is an attention mechanism relating different positions

of a single sequence in order to compute a representation of the sequence. Self-attention has been

used successfully in a variety of tasks including reading comprehension, abstractive summarization,

textual entailment and learning task-independent sentence representations [4, 27, 28, 22].

End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-

aligned recurrence and have been shown to perform well on simple-language question answering and

language modeling tasks [34].

To the best of our knowledge, however, the Transformer is the ﬁrst transduction model relying

entirely on self-attention to compute representations of its input and output without using sequence-

aligned RNNs or convolution. In the following sections, we will describe the Transformer, motivate

self-attention and discuss its advantages over models such as [17, 18] and [9].

3 Model Architecture

Most competitive neural sequence transduction models have an encoder-decoder structure [

Here, the encoder maps an input sequence of symbol representations

, ..., x

)

to a sequence

of continuous representations

z = (z

, ..., z

)

. Given

, the decoder then generates an output

sequence

, ..., y

)

of symbols one element at a time. At each step the model is auto-regressive

[10], consuming the previously generated symbols as additional input when generating the next.

Figure 1: The Transformer - model architecture.

The Transformer follows this overall architecture using stacked self-attention and point-wise, fully

connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1,

respectively.

3.1 Encoder and Decoder Stacks

Encoder: The encoder is composed of a stack of

N = 6

identical layers. Each layer has two

sub-layers. The ﬁrst is a multi-head self-attention mechanism, and the second is a simple, position-

wise fully connected feed-forward network. We employ a residual connection [

] around each of

the two sub-layers, followed by layer normalization [

]. That is, the output of each sub-layer is

LayerNorm(x + Sublayer(x))

, where

Sublayer(x)

is the function implemented by the sub-layer

itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding

layers, produce outputs of dimension d

model

= 512.

Decoder: The decoder is also composed of a stack of

N = 6

identical layers. In addition to the two

sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head

attention over the output of the encoder stack. Similar to the encoder, we employ residual connections

around each of the sub-layers, followed by layer normalization. We also modify the self-attention

sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This

masking, combined with fact that the output embeddings are offset by one position, ensures that the

predictions for position i can depend only on the known outputs at positions less than i.

3.2 Attention

An attention function can be described as mapping a query and a set of key-value pairs to an output,

where the query, keys, values, and output are all vectors. The output is computed as a weighted sum

Scaled Dot-Product Attention Multi-Head Attention

Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several

attention layers running in parallel.

of the values, where the weight assigned to each value is computed by a compatibility function of the

query with the corresponding key.

3.2.1 Scaled Dot-Product Attention

We call our particular attention "Scaled Dot-Product Attention" (Figure 2). The input consists of

queries and keys of dimension

, and values of dimension

. We compute the dot products of the

query with all keys, divide each by

√

, and apply a softmax function to obtain the weights on the

values.

In practice, we compute the attention function on a set of queries simultaneously, packed together

into a matrix

. The keys and values are also packed together into matrices

and

. We compute

the matrix of outputs as:

Attention(Q, K, V ) = softmax(

√

)V (1)

The two most commonly used attention functions are additive attention [

], and dot-product (multi-

plicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor

√

. Additive attention computes the compatibility function using a feed-forward network with

a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is

much faster and more space-efﬁcient in practice, since it can be implemented using highly optimized

matrix multiplication code.

While for small values of

the two mechanisms perform similarly, additive attention outperforms

dot product attention without scaling for larger values of

[

]. We suspect that for large values of

, the dot products grow large in magnitude, pushing the softmax function into regions where it has

extremely small gradients

. To counteract this effect, we scale the dot products by

√

3.2.2 Multi-Head Attention

Instead of performing a single attention function with

model

-dimensional keys, values and queries,

we found it beneﬁcial to linearly project the queries, keys and values

times with different, learned

linear projections to

and

dimensions, respectively. On each of these projected versions of

queries, keys and values we then perform the attention function in parallel, yielding

-dimensional

To illustrate why the dot products get large, assume that the components of

and

are independent random

variables with mean

and variance

. Then their dot product,

q · k =

i=1

, has mean

and variance

output values. These are concatenated and once again projected, resulting in the ﬁnal values, as

depicted in Figure 2.

Multi-head attention allows the model to jointly attend to information from different representation

subspaces at different positions. With a single attention head, averaging inhibits this.

MultiHead(Q, K, V ) = Concat(head

, ..., head

where head

= Attention(QW

, KW

, V W

)

Where the projections are parameter matrices

∈ R

model

×d

∈ R

model

×d

∈ R

model

×d

and W

∈ R

×d

model

In this work we employ

h = 8

parallel attention layers, or heads. For each of these we use

= d

model

/h = 64

. Due to the reduced dimension of each head, the total computational cost

is similar to that of single-head attention with full dimensionality.

3.2.3 Applications of Attention in our Model

The Transformer uses multi-head attention in three different ways:

•

In "encoder-decoder attention" layers, the queries come from the previous decoder layer,

and the memory keys and values come from the output of the encoder. This allows every

position in the decoder to attend over all positions in the input sequence. This mimics the

typical encoder-decoder attention mechanisms in sequence-to-sequence models such as

[38, 2, 9].

•

The encoder contains self-attention layers. In a self-attention layer all of the keys, values

and queries come from the same place, in this case, the output of the previous layer in the

encoder. Each position in the encoder can attend to all positions in the previous layer of the

encoder.

•

Similarly, self-attention layers in the decoder allow each position in the decoder to attend to

all positions in the decoder up to and including that position. We need to prevent leftward

information ﬂow in the decoder to preserve the auto-regressive property. We implement this

inside of scaled dot-product attention by masking out (setting to

−∞

) all values in the input

of the softmax which correspond to illegal connections. See Figure 2.

3.3 Position-wise Feed-Forward Networks

In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully

connected feed-forward network, which is applied to each position separately and identically. This

consists of two linear transformations with a ReLU activation in between.

FFN(x) = max(0, xW

+ b

(2)

While the linear transformations are the same across different positions, they use different parameters

from layer to layer. Another way of describing this is as two convolutions with kernel size 1.

The dimensionality of input and output is

model

= 512

, and the inner-layer has dimensionality

= 2048.

3.4 Embeddings and Softmax

Similarly to other sequence transduction models, we use learned embeddings to convert the input

tokens and output tokens to vectors of dimension

model

. We also use the usual learned linear transfor-

mation and softmax function to convert the decoder output to predicted next-token probabilities. In

our model, we share the same weight matrix between the two embedding layers and the pre-softmax

linear transformation, similar to [

]. In the embedding layers, we multiply those weights by

√

model

Table 1: Maximum path lengths, per-layer complexity and minimum number of sequential operations

for different layer types.

is the sequence length,

is the representation dimension,

is the kernel

size of convolutions and r the size of the neighborhood in restricted self-attention.

Layer Type Complexity per Layer Sequential Maximum Path Length

Operations

Self-Attention O(n

· d) O(1) O(1)

Recurrent O(n · d

) O(n) O(n)

Convolutional O(k · n · d

) O(1) O(log

(n))

Self-Attention (restricted) O(r · n · d) O(1) O(n/r)

3.5 Positional Encoding

Since our model contains no recurrence and no convolution, in order for the model to make use of the

order of the sequence, we must inject some information about the relative or absolute position of the

tokens in the sequence. To this end, we add "positional encodings" to the input embeddings at the

bottoms of the encoder and decoder stacks. The positional encodings have the same dimension

model

as the embeddings, so that the two can be summed. There are many choices of positional encodings,

learned and ﬁxed [9].

In this work, we use sine and cosine functions of different frequencies:

P E

(pos,2i)

= sin(pos/10000

2i/d

model

)

P E

(pos,2i+1)

= cos(pos/10000

2i/d

model

)

where

pos

is the position and

is the dimension. That is, each dimension of the positional encoding

corresponds to a sinusoid. The wavelengths form a geometric progression from

2π

10000 · 2π

. We

chose this function because we hypothesized it would allow the model to easily learn to attend by

relative positions, since for any ﬁxed offset

P E

pos+k

can be represented as a linear function of

P E

pos

We also experimented with using learned positional embeddings [

] instead, and found that the two

versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version

because it may allow the model to extrapolate to sequence lengths longer than the ones encountered

during training.

4 Why Self-Attention

In this section we compare various aspects of self-attention layers to the recurrent and convolu-

tional layers commonly used for mapping one variable-length sequence of symbol representations

, ..., x

)

to another sequence of equal length

, ..., z

)

, with

, z

∈ R

, such as a hidden

layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we

consider three desiderata.

One is the total computational complexity per layer. Another is the amount of computation that can

be parallelized, as measured by the minimum number of sequential operations required.

The third is the path length between long-range dependencies in the network. Learning long-range

dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the

ability to learn such dependencies is the length of the paths forward and backward signals have to

traverse in the network. The shorter these paths between any combination of positions in the input

and output sequences, the easier it is to learn long-range dependencies [

]. Hence we also compare

the maximum path length between any two input and output positions in networks composed of the

different layer types.

As noted in Table 1, a self-attention layer connects all positions with a constant number of sequentially

executed operations, whereas a recurrent layer requires

O(n)

sequential operations. In terms of

computational complexity, self-attention layers are faster than recurrent layers when the sequence

length

is smaller than the representation dimensionality

, which is most often the case with

sentence representations used by state-of-the-art models in machine translations, such as word-piece

[

] and byte-pair [

] representations. To improve computational performance for tasks involving

very long sequences, self-attention could be restricted to considering only a neighborhood of size

the input sequence centered around the respective output position. This would increase the maximum

path length to O(n/r). We plan to investigate this approach further in future work.

A single convolutional layer with kernel width

k < n

does not connect all pairs of input and output

positions. Doing so requires a stack of

O(n/k)

convolutional layers in the case of contiguous kernels,

O(log

(n))

in the case of dilated convolutions [

], increasing the length of the longest paths

between any two positions in the network. Convolutional layers are generally more expensive than

recurrent layers, by a factor of

. Separable convolutions [

], however, decrease the complexity

considerably, to

O(k · n · d + n · d

)

. Even with

k = n

, however, the complexity of a separable

convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer,

the approach we take in our model.

As side beneﬁt, self-attention could yield more interpretable models. We inspect attention distributions

from our models and present and discuss examples in the appendix. Not only do individual attention

heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic

and semantic structure of the sentences.

5 Training

This section describes the training regime for our models.

5.1 Training Data and Batching

We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million

sentence pairs. Sentences were encoded using byte-pair encoding [

], which has a shared source-

target vocabulary of about 37000 tokens. For English-French, we used the signiﬁcantly larger WMT

2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece

vocabulary [

]. Sentence pairs were batched together by approximate sequence length. Each training

batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000

target tokens.

5.2 Hardware and Schedule

We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using

the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We

trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the

bottom line of table 3), step time was 1.0 seconds. The big models were trained for 300,000 steps

(3.5 days).

5.3 Optimizer

We used the Adam optimizer [

] with

= 0.9

= 0.98

and

ϵ = 10

−9

. We varied the learning

rate over the course of training, according to the formula:

lrate = d

−0.5

model

· min(step_num

−0.5

, step_num · warmup_steps

−1.5

) (3)

This corresponds to increasing the learning rate linearly for the ﬁrst

warmup_steps

training steps,

and decreasing it thereafter proportionally to the inverse square root of the step number. We used

warmup_steps = 4000.

5.4 Regularization

We employ three types of regularization during training:

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 · 10

GNMT + RL [38] 24.6 39.92 2.3 · 10

1.4 · 10

ConvS2S [9] 25.16 40.46 9.6 · 10

1.5 · 10

MoE [32] 26.03 40.56 2.0 · 10

1.2 · 10

Deep-Att + PosUnk Ensemble [39] 40.4 8.0 · 10

GNMT + RL Ensemble [38] 26.30 41.16 1.8 · 10

1.1 · 10

ConvS2S Ensemble [9] 26.36 41.29 7.7 · 10

1.2 · 10

Transformer (base model) 27.3 38.1 3.3 · 10

Transformer (big) 28.4 41.8 2.3 · 10

Residual Dropout We apply dropout [

] to the output of each sub-layer, before it is added to the

sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the

positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of

drop

= 0.1.

Label Smoothing During training, we employed label smoothing of value

= 0.1

[

]. This

hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.

6 Results

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

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 P

drop

= 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

and length penalty

α = 0.6

[

]. 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

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

We used values of 2.8, 3.7, 6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.

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 d

model

h d

drop

train PPL BLEU params

steps (dev) (dev) ×10

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

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.

In Table 3 rows (B), we observe that reducing the attention key size

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 [

], 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

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

model

= 1024

on the Wall Street Journal (WSJ) portion of the

Penn Treebank [

], 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

[

]. 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

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

increased the maximum output length to input length +

300

. We used a beam size of

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 sur-

prisingly well, yielding better results than all previously reported models with the exception of the

Recurrent Neural Network Grammar [8].

In contrast to RNN sequence-to-sequence models [

], the Transformer outperforms the Berkeley-

Parser [29] even when training only on the WSJ training set of 40K sentences.

7 Conclusion

In this work, we presented the Transformer, the ﬁrst sequence transduction model based entirely on

attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with

multi-headed self-attention.

For translation tasks, the Transformer can be trained signiﬁcantly faster than architectures based

on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014

English-to-French translation tasks, we achieve a new state of the art. In the former task our best

model outperforms even all previously reported ensembles.

We are excited about the future of attention-based models and plan to apply them to other tasks. We

plan to extend the Transformer to problems involving input and output modalities other than text and

to investigate local, restricted attention mechanisms to efﬁciently handle large inputs and outputs

such as images, audio and video. Making generation less sequential is another research goals of ours.

The code we used to train and evaluate our models is available at

https://github.com/

tensorflow/tensor2tensor.

Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful

comments, corrections and inspiration.

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Attention Visualizations

Input-Input Layer5

this

spirit

that

majority

American

governments

have

passed

new

laws

since

2009

making

the

registration

voting

process

difficult

<EOS>

<pad>

this

spirit

that

majority

American

governments

have

passed

new

laws

since

2009

making

the

registration

voting

process

difficult

<EOS>

<pad>

Figure 3: An example of the attention mechanism following long-distance dependencies in the

encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of

the verb ‘making’, completing the phrase ‘making...more difﬁcult’. Attentions here shown only for

the word ‘making’. Different colors represent different heads. Best viewed in color.

Input-Input Layer5

The

Law

will

never

perfect

but

its

application

should

just

this

what

are

missing

opinion

<EOS>

<pad>

The

Law

will

never

perfect

but

its

application

should

just

this

what

are

missing

opinion

<EOS>

<pad>

Input-Input Layer5

The

Law

will

never

perfect

but

its

application

should

just

this

what

are

missing

opinion

<EOS>

<pad>

The

Law

will

never

perfect

but

its

application

should

just

this

what

are

missing

opinion

<EOS>

<pad>

Figure 4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top:

Full attentions for head 5. Bottom: Isolated attentions from just the word ‘its’ for attention heads 5

and 6. Note that the attentions are very sharp for this word.

Input-Input Layer5

The

Law

will

never

perfect

but

its

application

should

just

this

what

are

missing

opinion

<EOS>

<pad>

The

Law

will

never

perfect

but

its

application

should

just

this

what

are

missing

opinion

<EOS>

<pad>

Input-Input Layer5

The

Law

will

never

perfect

but

its

application

should

just

this

what

are

missing

opinion

<EOS>

<pad>

The

Law

will

never

perfect

but

its

application

should

just

this

what

are

missing

opinion

<EOS>

<pad>

Figure 5: Many of the attention heads exhibit behaviour that seems related to the structure of the

sentence. We give two such examples above, from two different heads from the encoder self-attention

at layer 5 of 6. The heads clearly learned to perform different tasks.