YAG – Real-Time Template-Based NLG System | NLG-Wiki

YAG – A Natural Language Generation (NLG) System Overview

What is YAG?

YAG is a real-time, general-purpose natural language generation (NLG) system developed to generate fluent text from structured inputs using an enriched template-based architecture.

Architecture and Key Features

YAG bridges traditional template-based NLG with deeper linguistic processes. While it uses templates, it incorporates attribute grammars and syntactic reasoning to handle subject-verb agreement, number, person and other linguistic variations.

Typical components include:

• Input: Structured fact records or event descriptions.

• Template library: Templates with placeholders, conditions and optional recursive structure.

• Attribute grammar rules: For morphological and syntactic adjustments (e.g., singular/plural, verb form).

• Output: Natural language sentences or paragraphs in real time.
  
  

Application Areas

YAG is designed for domains needing fast, reliable text generation from data, such as:

• Interactive tutoring systems and feedback generation

• Report generation, narrative summaries from database records

• Dialogue or explanation systems in educational contexts
  
  

Why YAG Matters

YAG demonstrates how modern template-based NLG systems can scale to real-time, general-purpose usage without fully shifting to neural models. Its hybrid design illustrates a middle ground between purely rule-based systems and statistical/textual approaches.

How to Use the YAG Entry on NLG-Wiki

• Navigate to the “Systems” section and select YAG.

• Review metadata: architecture type (template + attribute grammar), input/output types, domain examples.

• Check linked publications for deeper insights (e.g., McRoy, Channarukul & Ali 2003).

• Compare YAG with other listed systems to evaluate different NLG design trade-offs.
  
  

FAQ – YAG System

What is YAG used for?
YAG is used to generate natural-language text from structured data in real time, especially where linguistic variation matters.

Is YAG purely template-based?
While template-based, YAG integrates grammar rules and attribute processing to increase flexibility and linguistic accuracy.

Can YAG work for multiple languages?
In principle yes — its modular design allows adaptation, though most documented uses focus on English.

Where can I find more details?
See YAG’s entry on NLG-Wiki and referenced research articles.