Publikation
A System for Rapid Development of Large Scale Rule Bases for Template-Based NLG for Conversational Agents
Tim Krones
Mastersthesis, Saarland University, 11/2014.
Zusammenfassung
Long-term social interaction between conversational agents and users requires
adaptivity and variation of system output (Kruijff-Korbayová et al. 2011,
Kruijff-Korbayová et al. 2012): Agents should produce natural language output
that is appropriate and relevant, and should not repeat themselves in
recurring situations. In the context of the ALIZ-E project
(http://www.aliz-e.org), designing natural language output involves writing
rules which match abstract representations of situational knowledge to
appropriate verbal responses.
We present a new graphical system for collaborative creation, maintenance, and
long-term evolution of large-scale rule bases that addresses many challenges
involved in working with these rules: Specialized editing features facilitate
fast creation of large amounts of variation and help minimize errors that are
likely to occur when editing plain text representations of rules. By
abstracting away from native rule syntax, the system aims to make the process
of working with rules accessible to non-experts. Support for searching and
filtering rule bases in various ways facilitates maintenance and long-term
evolution. Results of a first set of evaluation experiments suggest that using
our system, non-experts can gain a basic conceptual understanding of rules and
productively design natural language output with relatively little training.