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7. Figures and tables that work

Many reviewers read your figures and tables before a single sentence of prose, and form an opinion there. Figure 1 — usually your architecture or your main idea — and Table 1 — usually your headline result — do more persuading than any paragraph. Methods tells you to spend a full day on Figure 1; Results tells you the main table is the headline. This chapter is how to make figures and tables that are correct, legible, accessible, and ACL-compliant — and which tools to reach for. The governing test: a reader who looks only at your figures, tables, and their captions should be able to reconstruct the paper.

What ACL actually requires​

These are not suggestions; getting them wrong looks careless to the people deciding your fate. From the ACL formatting guidelines:

  • Use vector graphics. "Graphics and photos should, if possible, use vector graphic formats (PDF, EPS), which allow the graphics to scale arbitrarily." A blurry PNG screenshot of a plot is the most common amateur tell — export PDF instead.
  • Keep the font large. "For any text or numbers in tables and figures, whenever possible, please use the font size of the document text." Six-point axis labels nobody can read are a real source of reviewer irritation.
  • Captions are 10-point and self-contained, and the ACL guide places them below the figure or table — but follow the year's style files, and make every caption a short paragraph that explains the figure without the body text (Results).
  • It must survive print and zoom. "Your paper must look good both when printed (A4 size) and when viewed onscreen as PDF." Check both.
  • Grayscale-readable. "To accommodate people who are color-blind (as well as those printing with black-and-white printers), grayscale readability is strongly encouraged… tables and figures [must] not rely solely on color to convey critical distinctions."

Color, done right​

Roughly eight percent of men have some color-vision deficiency, and plenty of reviewers still print in black and white — so color can never be your only signal.

  • Use a colorblind-safe palette. Good defaults: viridis, the Okabe–Ito palette, ColorBrewer schemes, or seaborn's "colorblind". Avoid distinguishing categories by red-versus-green alone.
  • Encode redundantly. Pair color with line style, marker shape, or a direct label, so the figure still works in grayscale. WCAG AA contrast (4.5:1 for normal text) is the accessibility target.
  • Test it. Run the figure through a simulator (Color Oracle, or the Coblis web tool) and literally print it in grayscale. If two lines merge, redesign before a reviewer does it for you.

Plots that work​

The classic short reference is Rougier et al.'s "Ten Simple Rules for Better Figures" (PLOS Computational Biology, 2014, open access); the deeper one is Claus Wilke's free Fundamentals of Data Visualization. The essentials:

  • One figure, one message. Decide the single point the plot must make, then strip everything that does not serve it. A figure that shows five things shows nothing.
  • Maximize data, minimize ink. Tufte's principle: no 3-D bars, no gradient fills, no heavy gridlines, no chartjunk. The data should dominate the frame.
  • Label honestly. Axes with units; a legend or, better, direct labels on the lines; error bars or shaded variance where you have multiple seeds.
  • Do not mislead with scale. Start bar charts at zero, never truncate an axis to inflate a gap, and use the same axis range across plots you want compared.
  • Tools. matplotlib + seaborn (Python) or ggplot2 (R); save with a vector backend (plt.savefig("fig.pdf", bbox_inches="tight")) and a colorblind palette. Generate the plot from your results files, not by hand, so it updates when the numbers do.

Architecture and method diagrams​

Figure 1 is often the most reproduced part of a paper, so it earns real time. A reader should be able to explain your method from the diagram alone.

  • Show flow. Lay it out left-to-right or top-down so the data path is obvious; use arrows for data flow and consistent shapes for consistent kinds of thing.
  • Label the tensors. Name inputs, outputs, and key dimensions; a reader should see where the shapes change.
  • Cut ruthlessly. A diagram with forty boxes communicates nothing. Show the idea; push the exhaustive detail to the text or an appendix figure.
  • Tools. TikZ/PGF is the publication standard (native LaTeX, vector, beautiful, steep learning curve); draw.io / diagrams.net and Excalidraw are fast and free and export PDF; Inkscape and Graphviz suit specific needs; Keynote or PowerPoint are fine if you export to vector PDF, not a screenshot. Whatever you draw in, export vector.

Tables that work​

A good results table is dense, scannable, and honest. The single highest-leverage fix in LaTeX is the booktabs package.

  • No vertical rules. Use booktabs' \toprule, \midrule, \bottomrule and no vertical lines and no double rules — they are visual clutter that the eye has to fight through.
  • Bold the best per column, and if the difference is not significant, bold both rather than implying a winner that the statistics do not support (Experimental Setup).
  • Show variance and align numbers. Report 78.4 ± 0.6, keep significant figures consistent down a column, put units in the header, and align on the decimal point (the siunitx S column does this automatically).
  • Right-align numbers, left-align text, group related rows, and let whitespace — not lines — do the separating.
  • Don't dump the giant table in the body. A 40-column monster belongs in the appendix; the main text gets the rows that carry your claims.
  • Tools. booktabs + siunitx for the styling; generate the table body from code (pandas df.to_latex() or df.style, or a small script) so the numbers in the paper can never drift from the numbers you computed.

Accessibility is now expected​

*ACL venues ask for accessible papers, and reviewers increasingly notice. Beyond the colorblind-safe, redundant-encoding rules above, the ACL accessibility guidance asks for alt text on every visual — figures, tables, charts, and diagrams alike. Write alt text that conveys the salient finding ("Accuracy rises with model size on all three languages, fastest for Hausa"), not a flat description ("a line chart"), and do not just repeat the caption. This is part of the work now, not optional polish.

Tools at a glance​

JobReach for
Statistical plotsmatplotlib + seaborn, ggplot2, plotly
Colorblind-safe colorviridis, Okabe–Ito, ColorBrewer, seaborn colorblind
Architecture diagramsTikZ/PGF, draw.io, Excalidraw, Inkscape, Graphviz
LaTeX tablesbooktabs, siunitx, tabularray, pandas to_latex
Checking color / grayscaleColor Oracle, Coblis, print it in B&W

A short workflow​

  • Draft the key figure and table early — deciding what Figure 1 and Table 1 must show forces clarity about your actual claim.
  • Generate from data, never hand-draw numbers, so everything updates when results change.
  • Export vector, embed fonts, and check the figure at 100% and zoomed in.
  • Run the grayscale and colorblind check, then write the alt text.
  • Read each caption alone. If it does not stand without the body text, it is not finished.

Common mistakes​

  • Raster screenshots. A blurry PNG of a plot or a table. Export vector PDF.
  • Tiny fonts. Axis labels and table numbers smaller than the body text.
  • Color as the only signal, or a red-green palette. Encode redundantly; use a safe palette.
  • Chartjunk and misleading axes. 3-D bars, truncated y-axes, mismatched scales across compared plots.
  • Vertical rules and double lines in tables. Use booktabs.
  • Label-only captions, and figures or tables the prose never refers to.
  • No alt text. Now an expected part of an accessible submission.

Further reading​