Difference between revisions of "Template Based Natural Language Generation"
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| − | This is a [[category::method]] of [[AddressesProblem::Natural Language Generation]]. | + | This is a [[category::method]] for the problem of [[AddressesProblem::Natural Language Generation|natural language generation]]. |
| + | In general, template-based systems are natural language generating (NLG) systems that map their non-linguistic input directly to linguistic structure. This structure may contain "gaps" which are filled in during output. | ||
| + | For example a simple template-based NLG system may take in some semantic representation like: | ||
| + | * Departure(Delta 2047, Gate B37, 15:00) | ||
| + | Which will in turn be associated directly with a given template, whose "gaps" are filled in with data from the semantic representation: | ||
| + | * '''[flight]''' will depart from '''[gate]''' at '''[time]'''. | ||
| + | |||
| + | The following are some modern template-based systems: | ||
| + | * TG/2 | ||
| + | * D2S | ||
| + | * EXEMPLARS | ||
| + | * YAG | ||
| + | * XTRAGEN | ||
| + | |||
| + | More sophisticated models of template-based NLG have been proposed to explore the following: | ||
| + | * Automatic methods of inducing templates | ||
| + | ** To address maintainability issues of template-based methods | ||
| + | * Making templates more domain independent. | ||
| + | ** To address the perception that template based methods are always domain specific | ||
| + | * Output quality & variability | ||
| + | |||
| + | |||
| + | == References == | ||
| + | * Real vs. template-based natural language generation: a false opposition? Deemter et al. CL 2003. | ||
== Relevant Papers == | == Relevant Papers == | ||
Latest revision as of 02:24, 31 March 2011
This is a method for the problem of natural language generation.
In general, template-based systems are natural language generating (NLG) systems that map their non-linguistic input directly to linguistic structure. This structure may contain "gaps" which are filled in during output.
For example a simple template-based NLG system may take in some semantic representation like:
- Departure(Delta 2047, Gate B37, 15:00)
Which will in turn be associated directly with a given template, whose "gaps" are filled in with data from the semantic representation:
- [flight] will depart from [gate] at [time].
The following are some modern template-based systems:
- TG/2
- D2S
- EXEMPLARS
- YAG
- XTRAGEN
More sophisticated models of template-based NLG have been proposed to explore the following:
- Automatic methods of inducing templates
- To address maintainability issues of template-based methods
- Making templates more domain independent.
- To address the perception that template based methods are always domain specific
- Output quality & variability
References
- Real vs. template-based natural language generation: a false opposition? Deemter et al. CL 2003.
