Abstract
This paper presents the first demonstration
of a statistical spoken dialogue system that
uses automatic belief compression to reason
over complex user goal sets. Reasoning
over the power set of possible user goals allows
complex sets of user goals to be represented,
which leads to more natural dialogues.
The use of the power set results in a
massive expansion in the number of belief
states maintained by the Partially Observable
Markov Decision Process (POMDP)
spoken dialogue manager. A modified form
of Value Directed Compression (VDC) is
applied to the POMDP belief states producing
a near-lossless compression which reduces
the number of bases required to represent
the belief distribution
of a statistical spoken dialogue system that
uses automatic belief compression to reason
over complex user goal sets. Reasoning
over the power set of possible user goals allows
complex sets of user goals to be represented,
which leads to more natural dialogues.
The use of the power set results in a
massive expansion in the number of belief
states maintained by the Partially Observable
Markov Decision Process (POMDP)
spoken dialogue manager. A modified form
of Value Directed Compression (VDC) is
applied to the POMDP belief states producing
a near-lossless compression which reduces
the number of bases required to represent
the belief distribution
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, Avignon, France, April 23-27 |
| Place of Publication | Stroudsburg, PA |
| Publisher | Association for Computational Linguistics |
| Pages | 46-50 |
| ISBN (Print) | 978-1-937284-19-0 |
| Publication status | Published - 2012 |