XtraGen – XML-Based Natural Language Generation System (NLG)

What Is XtraGen?

XtraGen is an XML- and Java-based Natural Language Generation system created by Holger Stenzhorn in 2002. It was designed as a lightweight, rule-driven NLG engine that could be integrated into real-time applications, offering more flexibility than simple templates and more speed than deep linguistic generators.

The system aims to bridge the gap between template NLG and grammar-driven generation, making it easier for developers to build configurable text-generation modules using open technologies.

Key Features of the XtraGen NLG System

XML Grammar Format

XtraGen uses an XML-encoded grammar for representing templates, constraints, and linguistic rules. This structure allows developers to:

• Organize NLG rules in modular XML files

• Reuse text fragments

• Add parameters and conditions for dynamic text creation

• Extend grammars for new domains

Java-Based NLG Engine

The generation engine is implemented in Java, enabling fast performance and simple integration with enterprise or UI systems. This helps XtraGen function as a real-time text generator for interactive applications.

Template + Constraint Architecture

Instead of plain canned text, XtraGen supports:

• Conditional templates

• Variable substitution

• Morphological inflection

• Backtracking and rule selection

• Constraint-based template retrieval

This makes it suitable for domains where text needs to be accurate, configurable, and consistent.

How XtraGen Works

The generation workflow includes:

1. Evaluating context conditions

2. Selecting valid templates based on constraints

3. Passing parameters and variables

4. Applying morphological rules

5. Producing a final, grammatically-correct text string

This structure is typical of rule-based NLG systems, but XtraGen’s XML approach makes rule editing more accessible.

Use Cases and Applications

While not tied to a single domain, XtraGen can be used in:

• Dialogue systems

• Automated reporting tools

• Knowledge-based systems

• Human–computer interaction interfaces

• Educational or enterprise software

Its design prioritizes stability, predictability, and domain-expert control, which many modern neural systems struggle to offer.

Advantages of XtraGen

• Uses open standards (XML + Java)

• Easy to integrate into existing systems

• More flexible than canned templates

• Suitable for real-time use

• Good for domain-specific or explanation-based text generation

• A key example of early 2000s NLG engineering best practices

Limitations

• Not intended for open-domain generation

• Requires manual grammar authoring

• Lacks statistical or neural learning capabilities

• Best suited for small to medium-sized domains

Why XtraGen Matters in the History of NLG

XtraGen represents an important stage in rule-based NLG evolution, demonstrating how:

• XML grammars

• modular templates

• and Java runtime engines

...could be combined to build maintainable and scalable NLG systems before the rise of neural text generation.

For researchers studying NLG history, architecture choices, and hybrid systems, XtraGen provides a clear, well-documented example.