Pictorial and Verbal Tools for Conveying Routes
COSIT '99 Proceedings of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science
When and Why Are Visual Landmarks Used in Giving Directions?
COSIT 2001 Proceedings of the International Conference on Spatial Information Theory: Foundations of Geographic Information Science
The WAMI toolkit for developing, deploying, and evaluating web-accessible multimodal interfaces
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Walk the talk: connecting language, knowledge, and action in route instructions
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
OpenFst: a general and efficient weighted finite-state transducer library
CIAA'07 Proceedings of the 12th international conference on Implementation and application of automata
Probabilistic ontology trees for belief tracking in dialog systems
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Cultural differences of spatial descriptions in tourist guidebooks
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
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Many modern spoken dialog systems use probabilistic graphical models to update their belief over the concepts under discussion, increasing robustness in the face of noisy input. However, such models are ill-suited to probabilistic reasoning about spatial relationships between entities. In particular, a car navigation system that infers users' intended destination using nearby landmarks as descriptions must be able to use distance measures as a factor in inference. In this paper, we describe a belief tracking system for a location identification task that combines a semantic belief tracker for categorical concepts based on the DPOT framework (Raux and Ma, 2011) with a kernel density estimator that incorporates landmark evidence from multiple turns and landmark hypotheses, into a posterior probability over candidate locations. We evaluate our approach on a corpus of destination setting dialogs and show that it significantly outperforms a deterministic baseline.