How to Write an Academic Paper Review in English: Complete Guide to Structure, Expressions, and Examples

Introduction

Reading academic papers and reviewing them in English is an essential skill for researchers, graduate students, and tech bloggers. However, many people struggle with questions like “What structure should I use?”, “I’m stuck on English expressions”, or “How much should I summarize versus critique?”

This article covers a systematic approach to writing paper reviews in English. It includes everything from structuring strategies to essential expressions for each section and practical examples.

Key Point: A good paper review balances summary and critical analysis.

Purpose and Types of Paper Reviews

3 Purposes of Paper Reviews

1. Study Note: Organize your understanding and reference it later
2. Knowledge Sharing: Blogs, seminar presentations, etc.
3. Peer Review: Journal reviews, research group discussions

Characteristics by Review Type

This article focuses on the most widely used Critical Review.

Standard Structure of Paper Reviews

English paper reviews typically consist of the following 6 sections.

1. Metadata

Specify the basic information of the paper.

**Title**: Attention Is All You Need  
**Authors**: Vaswani et al.  
**Conference/Journal**: NeurIPS 2017  
**Paper Link**: [arXiv:1706.03762](https://arxiv.org/abs/1706.03762)  
**Code**: [GitHub](https://github.com/tensorflow/tensor2tensor)  

2. Summary

Compress the paper’s core ideas into 2-3 paragraphs.

Essential elements:
– Problem Statement
– Proposed Method
– Key Contribution

Example expressions:

This paper addresses the problem of...
The authors propose a novel approach based on...
The main contribution is threefold: (1)... (2)... (3)...

3. Background & Motivation

Explain why this research was needed and what limitations existed in previous methods.

Example expressions:

Previous works rely heavily on..., which suffers from...
To overcome this limitation, the authors...
Motivated by recent advances in...

4. Methodology

Analyze the paper’s core algorithms, architecture, and formulas in detail.

4.1 Architecture

Illustrate or describe the model structure.

Example:

The Transformer architecture consists of:
- **Encoder**: 6 identical layers with multi-head self-attention
- **Decoder**: 6 layers with masked self-attention and encoder-decoder attention

4.2 Loss Function

Express and interpret the optimization objective as formulas.

Example:

The model is trained using cross-entropy loss:

$$
\mathcal{L} = -\sum_{i=1}^{N} y_i \log(\hat{y}_i)
$$

where:
- $y_i$: ground truth label
- $\hat{y}_i$: predicted probability
- $N$: number of samples

Tip: Always explain what each term in the formula means.

4.3 Training Details

Organize hyperparameters, datasets, and optimization techniques.

5. Experimental Results

Interpret and analyze the paper’s experimental results.

5.1 Main Results

Example expressions:

The proposed method achieves state-of-the-art performance on...
Compared to the baseline, it improves BLEU score by 2.0 points.

Comparison table example:

5.2 Ablation Study

Analyze which components contributed to performance.

Example expressions:

The ablation study shows that removing multi-head attention degrades performance by 1.5 BLEU points, confirming its importance.

6. Strengths & Limitations

Objectively evaluate the paper’s pros and cons.

Strengths

Example expressions:

- **Novel approach**: First fully attention-based architecture
- **Efficiency**: Parallelizable, faster training than RNNs
- **Generalizability**: Applicable to various tasks (NLP, Vision)
- **Reproducibility**: Code and hyperparameters provided

Limitations

Example expressions:

- **Memory consumption**: Quadratic complexity with sequence length
- **Long sequences**: Performance degrades on very long inputs (>1000 tokens)
- **Limited evaluation**: Only tested on machine translation
- **Interpretability**: Attention weights are hard to interpret

Essential English Expressions by Section

Summary Section

• This paper proposes / introduces / presents a novel…
• The authors tackle / address the problem of…
• The main contribution / novelty / innovation is…
• The key idea is to leverage / exploit / utilize…

Methodology Section

• The model consists of / comprises / is composed of…
• The architecture is based on / built upon / inspired by…
• The loss function is defined as / formulated as…
• Formally, the objective can be written as…

Results Section

• The method achieves / attains / obtains state-of-the-art…
• It outperforms / surpasses / exceeds the baseline by…
• The results demonstrate / show / indicate that…
• Surprisingly, the model…

Strengths Section

• A major strength / key advantage is…
• The paper excels at / stands out for…
• Notably, the authors provide…
• The approach is well-motivated / theoretically grounded…

Limitations Section

• A potential weakness / limitation is…
• The method suffers from / struggles with…
• However, it fails to address…
• The evaluation is limited to / confined to…
• It would be interesting / valuable to investigate…

Practical Example: Transformer Paper Review

Below is an abbreviated review example of the “Attention Is All You Need” paper.

# Paper Review: Attention Is All You Need

**Authors**: Vaswani et al.  
**Conference**: NeurIPS 2017  
**Link**: [arXiv:1706.03762](https://arxiv.org/abs/1706.03762)

---

## Summary

This paper introduces the **Transformer**, a novel neural architecture for sequence-to-sequence tasks that relies entirely on attention mechanisms, dispensing with recurrence and convolutions. The model achieves state-of-the-art results on machine translation benchmarks (WMT 2014 En-De and En-Fr) while being significantly more parallelizable than RNN-based models.

The key innovation is the **multi-head self-attention** mechanism, which allows the model to jointly attend to information from different representation subspaces at different positions. The Transformer also introduces positional encodings to inject sequence order information.

**Main contributions**:
1. First fully attention-based architecture
2. Superior performance on translation tasks
3. Faster training due to parallelization

---

## Methodology

### Architecture

The Transformer consists of:
- **Encoder**: 6 identical layers, each with multi-head self-attention and feed-forward networks
- **Decoder**: 6 layers with masked self-attention, encoder-decoder attention, and feed-forward networks

### Self-Attention Mechanism

The scaled dot-product attention is defined as:

$$
\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V
$$

where:
- $Q$: Query matrix
- $K$: Key matrix
- $V$: Value matrix
- $d_k$: Dimension of keys (scaling factor prevents gradient vanishing)

Multi-head attention projects $Q$, $K$, $V$ into $h$ different subspaces:

$$
\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1, ..., \text{head}_h)W^O
$$

where each head attends to different aspects of the input.

---

## Results

| Model | BLEU (En-De) | Training Time |
|-------|--------------|---------------|
| ByteNet | 23.8 | - |
| ConvS2S | 25.2 | - |
| **Transformer (Base)** | **27.3** | **3.5 days** |
| **Transformer (Big)** | **28.4** | **12 hours** |

The Transformer Big model achieves **28.4 BLEU on WMT En-De**, a new state-of-the-art at the time, using only 0.4× the training cost of previous best models.

**Ablation Study** shows:
- Removing multi-head attention → -1.5 BLEU
- Removing positional encoding → -2.3 BLEU

---

## Strengths

1. **Parallelization**: Unlike RNNs, self-attention allows full parallelization across sequence positions
2. **Long-range dependencies**: Direct connections between all positions (vs. sequential in RNNs)
3. **Scalability**: Performance improves with model size and data
4. **Generalizability**: Now dominant in NLP, Vision (ViT), and Multimodal (CLIP) tasks

---

## Limitations

1. **Quadratic complexity**: Memory usage is $O(n^2)$ with sequence length $n$
2. **Long sequences**: Inefficient for sequences >1000 tokens (addressed by later work like Longformer, Linformer)
3. **Positional encoding**: Sinusoidal encoding is ad-hoc; learned embeddings may work better
4. **Interpretability**: Attention weights don't always align with human intuition

---

## Future Directions

- **Efficient attention**: Linear-time attention variants (e.g., Performers, Perceiver)
- **Vision Transformers**: Apply to image classification (ViT, DeiT)
- **Multimodal fusion**: Combine text, image, audio (CLIP, Flamingo)

---

## Conclusion

The Transformer is a landmark paper that fundamentally changed the landscape of deep learning. Its simplicity, efficiency, and effectiveness have made it the de facto standard for modern NLP and beyond.

Common Mistakes When Writing Paper Reviews

❌ What to Avoid

1. Excessive summarization: Don’t just translate the paper’s content.
   – ❌ “Section 3.1 describes…, Section 3.2 explains…”
   – ✅ “The key innovation lies in…”

2. Reviews without critique: Don’t just list strengths; mention limitations too.
   – ❌ “This paper is perfect.”
   – ✅ “While the method excels at…, it struggles with…”

3. Formula bombardment: Don’t just list formulas; add intuitive explanations.
   – ❌ (10 lines of formulas only)
   – ✅ “This equation optimizes… by balancing… and…”

4. Subjective language: Avoid emotional language.
   – ❌ “This method is amazing!”
   – ✅ “This method demonstrates strong performance on…”

✅ What to Do

1. Structure: Divide into clear sections (Summary, Methodology, Results, etc.)
2. Visualization: Use tables, diagrams, and comparison charts
3. Provide context: Explain the relationship with existing research
4. Critical thinking: Balance strengths and limitations
5. Reproducibility: Provide enough detail for readers to understand

Useful Resources

Where to Find Paper Review Examples

• Papers with Code: paperswithcode.com – Code + benchmark results
• Distill.pub: distill.pub – High-quality reviews with visualizations
• arXiv-sanity: arxiv-sanity-lite.com – Paper recommendations + community reviews
• Reddit r/MachineLearning: Paper discussion threads

Tools for Improving English Expressions

• Grammarly: Grammar correction
• DeepL Write: Natural English expressions
• Academic Phrasebank: Database of academic writing expressions
• Ref-N-Write: Paraphrasing tool specifically for academic papers

Paper Review Writing Checklist

Check the following items after writing.

• [ ] Metadata: Includes paper title, authors, venue/journal, and link
• [ ] Summary: Core ideas summarized in 3-4 paragraphs
• [ ] Problem Statement: Specifies what problem is being solved
• [ ] Methodology: Structured explanation of methodology (Architecture, Loss, Training)
• [ ] Results: Main experimental results organized in tables/graphs
• [ ] Ablation Study: Analysis of which components are important
• [ ] Strengths: At least 3 strengths mentioned
• [ ] Limitations: At least 2 limitations identified
• [ ] Future Work: Suggests directions for follow-up research
• [ ] Clarity: Explained at a level understandable to non-experts
• [ ] Citations: Related papers appropriately cited

Learning Roadmap by Level

Beginner (0-6 months)

1. Summary practice: Rewrite paper abstracts in English
2. Structure learning: Analyze 3 well-written reviews to understand structure
3. Use templates: Use this article’s structure as a template

Intermediate (6-12 months)

1. Critical reading: Find limitations in papers yourself
2. Comparative analysis: Write reviews comparing 2-3 papers on the same topic
3. Formula interpretation: Explain key formulas in your own words

Advanced (12+ months)

1. Comprehensive reviews: Write survey-level reviews of specific fields
2. Reproduction experiments: Directly reproduce and analyze paper experiments
3. Journal reviewing: Participate in actual paper reviews (Peer Review)

Conclusion

Writing paper reviews in English is not just translation but a 3-step process of understanding → summarizing → critiquing.

Key Takeaways:
1. Structure: Follow the order Summary – Background – Methodology – Results – Strengths/Limitations
2. Clarity: Use appropriate expressions for each section (propose, demonstrate, outperform, etc.)
3. Balance: Distribute 50:50 between summary and critique
4. Visualization: Enhance understanding with tables, formulas, and comparison charts
5. Critical thinking: Always include 3 strengths + 2 limitations

Use this guide as a template to develop your own paper review style. It takes time at first, but after writing about 10 reviews, it becomes natural.

Recommendation: Choose 1 paper in your research field each week and write a review. You’ll feel significant improvement after 6 months.

Next Steps:
– Print the checklist from this article and post it on your wall
– Try writing a review of a recently read paper using this structure
– Read 3 reviews in your field of interest on Papers with Code and analyze their style

Paper review skills are the core of research capabilities that flow from reading → writing → presenting. With consistent practice, you can understand papers faster and more deeply!