Graph-based generation of referring expressions
Computational Linguistics
Enabling technology for multilingual natural language generation: the KPML development environment
Natural Language Engineering
Generating referring expressions involving relations
EACL '91 Proceedings of the fifth conference on European chapter of the Association for Computational Linguistics
An algorithm for generating referential descriptions with flexible interfaces
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
Incremental generation of spatial referring expressions in situated dialog
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Generating Referring Expressions: Making Referents Easy to Identify
Computational Linguistics
Crossmodal content binding in information-processing architectures
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Conceptual spatial representations for indoor mobile robots
Robotics and Autonomous Systems
Salience-driven Contextual Priming of Speech Recognition for Human-Robot Interaction
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A conceptual graph approach to the generation of referring expressions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Anchor-progression in spatially situated discourse: a production experiment
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Hi-index | 0.00 |
In this paper we present an approach to the task of generating and resolving referring expressions (REs) for conversational mobile robots. It is based on a spatial knowledge base encompassing both robot- and human-centric representations. Existing algorithms for the generation of referring expressions (GRE) try to find a description that uniquely identifies the referent with respect to other entities that are in the current context. Mobile robots, however, act in large-scale space, that is environments that are larger than what can be perceived at a glance, e.g. an office building with different floors, each containing several rooms and objects. One challenge when referring to elsewhere is thus to include enough information so that the interlocutors can extend their context appropriately. We address this challenge with a method for context construction that can be used for both generating and resolving REs - two previously disjoint aspects. Our approach is embedded in a bi-directional framework for natural language processing for robots.