Publikation
Introspection and Adaptable Model Integration for Dialogue-based Question Answering
Daniel Sonntag
In: Proceedings of the Twenty-first International Joint Conferences on Artificial Intelligence (IJCAI). International Joint Conference on Artificial Intelligence (IJCAI-09), July 11-17, Pasadena, CA, USA, Online, 2009.
Zusammenfassung
Dialogue-based Question Answering (QA) is a
highly complex task that brings together a QA system
including various natural language processing
components (i.e., components for question classification,
information extraction, and retrieval) with
dialogue systems for effective and natural communication.
The dialogue-based access is difficult to
establish when the QA system in use is complex
and combines many different answer services with
different quality and access characteristics. For
example, some questions are processed by opendomain
QA services with a broad coverage. Others
should be processed by using a domain-specific
instance ontology for more reliable answers. Different
answer services may change their characteristics
over time and the dialogue reaction models
have to be updated according to that. To solve
this problem, we developed introspective methods
to integrate adaptable models of the answer services.
We evaluated the impact of the learned models
on the dialogue performance, i.e., whether the
adaptable models can be used for a more convenient
dialogue formulation process. We show significant
effectiveness improvements in the resulting
dialogues when using the machine learning (ML)
models. Examples are provided in the context of
the generation of system-initiative feedback to user
questions and answers, as provided by heterogeneous
information services.