academic-writing-refiner

Academic Writing Refiner

Overview

Academic papers are judged not only on their scientific merit but also on the quality of their English. Grammatical errors, awkward phrasing, and non-native patterns (commonly called "Chinglish" when originating from Chinese-English interference) can distract reviewers and undermine credibility.

This skill provides a systematic checklist for identifying and correcting common academic English issues. It draws from the academic-writing-guide repository (327+ stars) maintained by SYSUSELab, which catalogs frequent mistakes observed in student and researcher manuscripts across STEM disciplines.

The approach is checklist-driven: rather than relying solely on automated tools, researchers learn to recognize error patterns and self-edit effectively. This skill is especially useful for non-native English speakers preparing manuscripts for international journals and conferences.

Common Error Categories

Grammar and Syntax Errors

Chinglish Patterns

These are interference patterns common when translating from Chinese thought patterns into English:

1. Topic-comment structure. Chinese allows "As for X, Y does Z." English prefers "Y does Z to X."

   • Wrong: "As for the dataset, we use ImageNet."
   • Better: "We use ImageNet as our dataset."

2. Redundant verbs. Chinese often uses verb-verb compounds that become redundant in English.

   • Wrong: "We can be able to achieve..."
   • Better: "We can achieve..."

3. Missing determiners. Chinese has no articles, leading to dropped "the/a/an."

   • Wrong: "Model achieves state-of-art result."
   • Better: "The model achieves a state-of-the-art result."

4. Overuse of "respectively."

   • Wrong: "The accuracy and F1 are 95% and 0.93 respectively."
   • Better: "The accuracy is 95% and the F1 score is 0.93."

5. "With the development of..." This opening is overused to the point of cliche.

   • Better: Lead with the specific problem or finding.

Self-Editing Checklist

Apply this checklist before submitting any manuscript:

Pass 1: Structure and Flow

• Each paragraph has a clear topic sentence.
• Transitions connect paragraphs logically (however, therefore, in contrast).
• No paragraph exceeds 200 words.
• Figures and tables are referenced in order.

Pass 2: Grammar and Mechanics

• All verbs agree with their subjects.
• Tense is consistent within each section (past for Methods/Results, present for general truths).
• Every acronym is defined on first use.
• No comma splices or run-on sentences.
• Articles (a, an, the) are used correctly.

Pass 3: Style and Conciseness

• Remove "very," "really," "quite," "basically" unless essential.
• Replace "in order to" with "to."
• Replace "a large number of" with "many" or give the exact count.
• Convert passive voice to active where possible.
• Eliminate "it is well known that" and similar filler phrases.

Pass 4: Technical Accuracy

• All numbers have units.
• Table column headers are unambiguous.
• Equations are numbered and referenced.
• Statistical claims include confidence intervals or p-values.

Automated Polishing Tools

While manual review is irreplaceable, these tools serve as a useful second pass:

# LanguageTool CLI for grammar checking
java -jar languagetool-commandline.jar -l en-US paper.tex

# Writefull for academic-specific suggestions (VS Code extension)
# Install from VS Code marketplace: "Writefull for LaTeX"

# textlint for rule-based prose linting
npm install -g textlint textlint-rule-no-dead-link textlint-rule-write-good
textlint paper.md

Tool Comparison

Sentence-Level Revision Examples

Before and After

Before: "In recent years, with the rapid development of deep learning, more and more researchers have paid attention to the problem of image classification, which is a very important task in computer vision."

After: "Image classification is a fundamental task in computer vision. Recent advances in deep learning have renewed interest in this problem, with convolutional and transformer architectures achieving human-level accuracy on standard benchmarks."

Before: "We can observe from Table 1 that our method can achieve better performance than baseline methods in terms of all evaluation metrics."

After: "Our method outperforms all baselines across every metric (Table 1)."

Best Practices

• Read published papers in your target venue. Absorb the register and conventions of journals like Nature, IEEE TPAMI, or ACL.
• Have a native speaker review critical sections. At minimum, ask someone to read the Abstract and Introduction.
• Use consistent terminology. If you call it "feature extraction" in Section 2, do not switch to "representation learning" in Section 4 unless you define the distinction.
• Limit sentences to 25 words on average. Long sentences are harder to parse, especially for non-native readers.
• Revise in separate passes. Do not try to fix grammar, structure, and style simultaneously.

References

• academic-writing-guide -- Writing checklist and tutorials (327+ stars)
• Common Errors in Technical Writing -- Henning Schulzrinne, Columbia University
• Writefull -- AI writing assistant trained on published papers
• LanguageTool -- Open-source grammar checker