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Title

Attention Is All You Need

Review Date

Nov 3, 2025

Completed in

499.56 seconds

Overall Summary

Manuscript requires substantial revisions focusing on clarity, consistency, and adherence to academic standards across multiple sections. Key issues include structural organization, acroynm usage, and completeness of metadata.

Issue Severity Breakdown

3

Critical Issues

25

Major Issues

5

Minor Issues

Language Quality

Overall Language Score

A-

Language Summary

The manuscript demonstrates strong academic language, with a few minor grammatical and syntactical issues that do not significantly impede overall clarity or flow.

Category Assessments

Grammar and Syntax

B+

Generally sound grammar and syntax, with occasional minor errors needing correction for enhanced precision.

Clarity and Precision

B

Ideas are communicated clearly, though some phrasing could be more precise and less ambiguous.

Conciseness

B+

The writing is largely concise, but some instances of wordiness or redundancy can be further refined.

Academic Tone

A

Maintains a consistently formal and scholarly tone appropriate for an academic publication.

Consistency

B+

Mostly consistent in terminology and formatting, with minor exceptions needing attention.

Readability and Flow

B+

The text flows logically, with good transitions, though sentence structure variation could be improved.

Strengths

Clear and effective communication of complex technical concepts.

Appropriate and consistent academic tone throughout the document.

Logical organization and structure of information.

Areas for Improvement

Occasional minor grammatical errors, such as missing articles.

Some instances of phrasing that could be more precise or less verbose.

Minor inconsistencies in referencing or terminology require attention.

Detailed Suggestions

Critical issues (3)

SUGGESTED IMPROVEMENT

Keywords: Transformer architecture; attention mechanism; neural machine translation; sequence transduction; deep learning; parallelization

EXPLANATION

No keywords were provided in the document. Based on the title 'Attention Is All You Need' and the abstract, the paper introduces the 'Transformer' architecture, which relies solely on 'attention mechanisms' and dispenses with recurrence and convolutions for 'sequence transduction' tasks like 'neural machine translation'. It highlights improved 'parallelization' and reduced training time, which are key contributions in 'deep learning'. Therefore, these keywords are suggested to accurately represent the paper's core contributions and technical focus.

ORIGINAL TEXT

Ashish Vaswani

EXPLANATION

No corresponding author was found. Please specify a corresponding author.

ORIGINAL TEXT

The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)

SUGGESTED IMPROVEMENT

Fig. 1: The Transformer generalizes well to English constituency parsing. Results are shown in Section 23 of the WSJ dataset.

EXPLANATION

The table 'tab:parsing-results' must be cited in the text. Additionally, clarify 'WSJ' as a dataset and add a figure number and description to the caption.

Major issues (25)

ORIGINAL TEXT

illia.polosukhin@gmail.com

EXPLANATION

A personal email address (@gmail.com) is used. It is recommended to use an institutional email address for academic publications to ensure professional correspondence.

ORIGINAL TEXT

University of Toronto

EXPLANATION

The institutional affiliation for Aidan N. Gomez is incomplete. Please add the department, city/state/province, and country to ensure the affiliation is complete.

ORIGINAL TEXT

Google Research

EXPLANATION

The institutional affiliation for several authors (Niki Parmar, Jakob Uszkoreit, Llion Jones, Illia Polosukhin) is incomplete. Please add the city/state/province and country for each author's affiliation to ensure completeness.

ORIGINAL TEXT

Google Brain

EXPLANATION

The institutional affiliation 'Google Brain' is incomplete for multiple authors (Ashish Vaswani, Noam Shazeer, Łukasz Kaiser). Please add the city, state/province, and country to ensure the affiliation is complete.

ORIGINAL TEXT

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.

EXPLANATION

The acronym 'Transformer' is defined multiple times. Remove this redundant definition (first defined at 5th paragraph of section 'Introduction': '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.'). Note: This definition ('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') differs from the initial definition ('a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output'). Use consistent terminology.

ORIGINAL TEXT

Background

EXPLANATION

The 'Background' section appears before the 'Introduction'. Typically, the Introduction should set the stage and provide context, followed by a more detailed background if necessary. Consider merging 'Background' into 'Introduction' or reordering if 'Background' presents foundational knowledge distinct from the paper's specific problem statement.

ORIGINAL TEXT

Model Architecture

EXPLANATION

The 'Model Architecture' section details the model's components, including attention mechanisms. However, there's a separate top-level section titled 'Why Self-Attention'. The content of 'Why Self-Attention' might be better integrated into the 'Model Architecture' section, specifically within the 'Attention' subsection, to provide justification and context for the chosen architecture.

ORIGINAL TEXT

Training

EXPLANATION

The 'Training' section is placed after 'Why Self-Attention'. Standard academic structure typically places 'Methods' or 'Experimental Setup' before 'Results'. The 'Training' section describes aspects of the methodology. Consider reordering to place 'Model Architecture' and 'Training' sections together as methodology before the 'Results' section.

ORIGINAL TEXT

Attention Visualizations

EXPLANATION

The 'Attention Visualizations' section is currently a top-level section with no content and appears after the 'Conclusion'. Visualizations are typically part of the 'Results' or 'Discussion' section to illustrate findings. If these visualizations are key results, they should be integrated into the 'Results' section. If they serve a supplementary purpose, they could be moved to an appendix.

ORIGINAL TEXT

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.

SUGGESTED IMPROVEMENT

Fig. 1: Examples of attention heads exhibiting sentence structure-related behavior from the encoder self-attention at layer 5 of 6. The heads learned to perform different tasks.

EXPLANATION

Added a figure number (Fig. 1) and specified that the examples are from a figure. Consolidated the descriptive sentences into a more concise caption.

ORIGINAL TEXT

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.

SUGGESTED IMPROVEMENT

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 (n=X).

EXPLANATION

Added missing essential information: sample size (n=X) for statistical data.

ORIGINAL TEXT

The Transformer - model architecture.

SUGGESTED IMPROVEMENT

Fig. 1: The Transformer : model architecture.

EXPLANATION

The figure 'fig:model-arch' needs to be cited in the text. Additionally, ensure consistent terminology when defining the 'Transformer' acronym and remove any redundant definitions. For improved caption formatting, change the hyphen to a colon.

ORIGINAL TEXT

Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. $n$ is the sequence length, $d$ is the representation dimension, $k$ is the kernel size of convolutions and $r$ the size of the neighborhood in restricted self-attention.

SUGGESTED IMPROVEMENT

Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. $n$ is the sequence length, $d$ is the representation dimension, $k$ is the kernel size of convolutions and $r$ the size of the neighborhood in restricted self-attention (e.g., Performer).

EXPLANATION

The table 'tab:op_complexities' should be cited in the text. Additionally, to clarify the context of 'restricted self-attention', an example layer type such as 'Performer' can be added.

ORIGINAL TEXT

(left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.

SUGGESTED IMPROVEMENT

Fig. 1: (left) Scaled Dot-Product Attention mechanism. (right) Multi-Head Attention mechanism, which consists of several attention layers running in parallel to capture different aspects of the input sequence.

EXPLANATION

The figure 'fig:multi-head-att' must be cited in the text. Additionally, the caption needs enhancement to explicitly state that both panels represent mechanisms and to provide more context for the Multi-Head Attention, such as its purpose in capturing different aspects of the input sequence.

ORIGINAL TEXT

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.

SUGGESTED IMPROVEMENT

The Transformer achieves better BLEU scores (x.xx) 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.

EXPLANATION

The table 'tab:wmt-results' should be cited in the text. Additionally, the specific BLEU score values for the Transformer model need to be provided.

ORIGINAL TEXT

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.

SUGGESTED IMPROVEMENT

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.

EXPLANATION

The table 'tab:variations' should be cited in the text. For clarity, 'newstest2013' has been enclosed in parentheses as it specifies the development set.

ORIGINAL TEXT

fact that

SUGGESTED IMPROVEMENT

the fact that

EXPLANATION

Missing article 'the' before 'fact'.

ORIGINAL TEXT

many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.

SUGGESTED IMPROVEMENT

many appear to exhibit behavior related to the syntactic and semantic structures of the sentences.

EXPLANATION

Changed 'structure' to 'structures' to agree with the plural subjects 'syntactic and semantic'.

ORIGINAL TEXT

section~ ef{sec:reg}

SUGGESTED IMPROVEMENT

section 22

EXPLANATION

The text mentions 'section~ ef{sec:reg}' and 'Section 22' separately. Assuming 'section~ ef{sec:reg}' refers to 'Section 22', this unifies the reference. If they are different sections, further clarification is needed.

ORIGINAL TEXT

corpora from with

SUGGESTED IMPROVEMENT

corpora with

EXPLANATION

Removed redundant word 'from'.

ORIGINAL TEXT

Making generation less sequential is another research goals of ours.

SUGGESTED IMPROVEMENT

Making generation less sequential is another of our research goals.

EXPLANATION

Corrected subject-verb agreement and phrasing: 'is another research goals of ours' to 'is another of our research goals'.

EXPLANATION

No funding statement was found. A funding statement briefly acknowledges the financial support behind a research project. It typically mentions the funding agency, the grant number, and sometimes the program name. It's usually placed in the acknowledgments or before the references. For example: "This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 programme (Grant agreement No. 758892)." or "The research was funded by the National Institutes of Health (NIH) under Grant R01 GM123456."

ORIGINAL TEXT

Attention Is All You Need

SUGGESTED IMPROVEMENT

The Transformer: Attention Is All You Need

EXPLANATION

The title should be more descriptive. Consider adding 'Transformer' to clearly identify the model architecture, as the paper introduces a novel sequence transduction model based solely on attention mechanisms.

ORIGINAL TEXT

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.

SUGGESTED IMPROVEMENT

On the Workshop on Machine Translation (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.

EXPLANATION

Define the acronym WMT upon first use. Additionally, ensure the abstract accurately reflects the paper's results, as there is a discrepancy between the abstract's stated BLEU score (41.8) and the score mentioned in the 'Machine Translation' section (41.0 for the big model).

ORIGINAL TEXT

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.

SUGGESTED IMPROVEMENT

Our model achieves 28.4 BLEU on the Workshop on Machine Translation (WMT) 2014 English-to-German translation task, improving over the existing best results (including ensembles) by over 2 BLEU.

EXPLANATION

Define the acronym 'WMT' upon first use. Additionally, clarify the parenthetical phrase 'including ensembles' by using parentheses.

Minor issues (5)

ORIGINAL TEXT

The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions.

SUGGESTED IMPROVEMENT

The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and Convolutional Sequence to Sequence \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions.

EXPLANATION

The acronym 'ConvS2S' is undefined and used only 2 times. Write out the full term 'Convolutional Sequence to Sequence' at each occurrence instead.

ORIGINAL TEXT

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.

SUGGESTED IMPROVEMENT

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 Convolutional Sequence to Sequence and logarithmically for ByteNet.

EXPLANATION

The acronym 'ConvS2S' is undefined and used only 2 times. Write out the full term 'Convolutional Sequence to Sequence' at each occurrence instead.

ORIGINAL TEXT

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

SUGGESTED IMPROVEMENT

This consists of two linear transformations with a Rectified Linear Unit activation in between.

EXPLANATION

The acronym 'ReLU' is undefined and used only 1 times. Write out the full term 'Rectified Linear Unit' at each occurrence instead.

ORIGINAL TEXT

We used the Adam optimizer~\citep{kingma2014adam} with $\beta_1=0.9$, $\beta_2=0.98$ and $\epsilon=10^{-9}$.

SUGGESTED IMPROVEMENT

We used the Adaptive moment estimation optimizer~\citep{kingma2014adam} with $\beta_1=0.9$, $\beta_2=0.98$ and $\epsilon=10^{-9}$.

EXPLANATION

The acronym 'Adam' is undefined and used only 1 times. Write out the full term 'Adaptive moment estimation' at each occurrence instead.

ORIGINAL TEXT

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.

SUGGESTED IMPROVEMENT

Fig. 1: Example of the attention mechanism highlighting long-distance dependencies in the encoder self-attention layer 5 of 6. Many attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions shown for the word 'making'. Different colors represent different heads. Best viewed in color.

EXPLANATION

Added figure number (Fig. 1) at the beginning of the caption, as is standard for figures in LaTeX documents. Removed extraneous backticks around 'making' and 'making...more difficult' for standard English punctuation.

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