Contextual revision in information seeking conversation systems

Contextual revision allows users to make incremental revisions to their previous query rather than providing a complete query each time. This is a powerful capability of conversation systems that can greatly reduce the word length of users' input, thereby improving speech recognition accuracy and simplifying NLU. We describe a novel method combining linguistic cues, semantic constraints, and data characteristics to address the ambiguities introduced by this construct.