What can I say?: evaluating a spoken language interface to Email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Context-Sensitive Help for Multimodal Dialogue
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Forest-based statistical sentence generation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Two-way adaptation for robust input interpretation in practical multimodal conversation systems
Proceedings of the 10th international conference on Intelligent user interfaces
Enabling context-sensitive information seeking
Proceedings of the 11th international conference on Intelligent user interfaces
Statistical acquisition of content selection rules for natural language generation
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
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Instance-based sentence boundary determination by optimization for natural language generation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Collective content selection for concept-to-text generation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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In this paper, we address a critical problem in conversation systems: limited input interpretation capabilities. When an interpretation error occurs, users often get stuck and cannot recover due to a lack of guidance from the system. To solve this problem, we present a hybrid natural language query recommendation framework that combines natural language generation with query retrieval. When receiving a problematic user query, our system dynamically recommends valid queries that are most relevant to the current user request so that the user can revise his request accordingly. Compared with existing methods, our approach offers two main contributions: first, improving query recommendation quality by combining query generation with query retrieval; second, adapting generated recommendations dynamically so that they are syntactically and lexically consistent with the original user input. Our evaluation results demonstrate the effectiveness of this approach.