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Author Intent: Eliminating Ambiguity in MathML

David Carlisle, Paul Libbrecht, Moritz Schubotz, Neil Soiffer

TL;DR

This paper tackles ambiguity in MathML speech by introducing author intent in MathML 4, enabling authors to annotate expressions with an intent attribute and arg references so assistive technologies can render semantically meaningful speech such as $|x|$ as "absolute value of x" rather than the literal "vertical bar x vertical bar". It clarifies a core and open list of intent concepts, along with properties like fixity, and shows how intents integrate with both presentation and content MathML to improve accessibility without breaking existing markup. The authors present prototypes for generating and consuming intents (e.g., macros like \abs and tools like MathCAT) and discuss early adoption signals, challenges in author education, and the forward-looking nature of intent to handle new notations while legacy content remains heuristic-driven. Overall, the approach offers a principled, extensible path to precise, audience-appropriate spoken notation and braille for math on the web, with practical impact for visually impaired readers and STEM content authors.

Abstract

MathML has been successful in improving the accessibility of mathematical notation on the web. All major screen readers support MathML to generate speech, allow navigation of the math, and generate braille. A troublesome area remains: handling ambiguous notations such as \( \vert x\vert\). While it is possible to speak this syntactically, anecdotal evidence indicates most people prefer semantic speech such as ``absolute value of x'' or ``determinant of x'' instead of ``vertical bar x vertical bar'' when first hearing an expression. Several heuristics to infer semantics have improved speech, but ultimately, the author is the one who definitively knows how an expression is meant to be spoken. The W3C Math Working Group is in the process of allowing authors to convey their intent in MathML markup via an intent attribute. This paper describes that work.

Author Intent: Eliminating Ambiguity in MathML

TL;DR

This paper tackles ambiguity in MathML speech by introducing author intent in MathML 4, enabling authors to annotate expressions with an intent attribute and arg references so assistive technologies can render semantically meaningful speech such as as "absolute value of x" rather than the literal "vertical bar x vertical bar". It clarifies a core and open list of intent concepts, along with properties like fixity, and shows how intents integrate with both presentation and content MathML to improve accessibility without breaking existing markup. The authors present prototypes for generating and consuming intents (e.g., macros like \abs and tools like MathCAT) and discuss early adoption signals, challenges in author education, and the forward-looking nature of intent to handle new notations while legacy content remains heuristic-driven. Overall, the approach offers a principled, extensible path to precise, audience-appropriate spoken notation and braille for math on the web, with practical impact for visually impaired readers and STEM content authors.

Abstract

MathML has been successful in improving the accessibility of mathematical notation on the web. All major screen readers support MathML to generate speech, allow navigation of the math, and generate braille. A troublesome area remains: handling ambiguous notations such as . While it is possible to speak this syntactically, anecdotal evidence indicates most people prefer semantic speech such as ``absolute value of x'' or ``determinant of x'' instead of ``vertical bar x vertical bar'' when first hearing an expression. Several heuristics to infer semantics have improved speech, but ultimately, the author is the one who definitively knows how an expression is meant to be spoken. The W3C Math Working Group is in the process of allowing authors to convey their intent in MathML markup via an intent attribute. This paper describes that work.
Paper Structure (8 sections, 3 figures)

This paper contains 8 sections, 3 figures.

Table of Contents

  1. Background
  2. Author Intent in MathML 4
  3. Author Intent Basics
  4. Intent Concepts
  5. Intent Properties
  6. Intent and Content MathML
  7. Evaluating the Success of Intent
  8. Conclusions

Figures (3)

  • Figure 1: Examples of ambiguous notations
  • Figure 2: Simple intent example
  • Figure 3: Example of nested arguments in "intent"