Paper: Lightly Supervised Learning of Procedural Dialog Systems

ACL ID P13-1164
Title Lightly Supervised Learning of Procedural Dialog Systems
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Procedural dialog systems can help users achieve a wide range of goals. However, such systems are challenging to build, currently requiring manual engineering of substantial domain-specific task knowl- edge and dialog management strategies. In this paper, we demonstrate that it is pos- sible to learn procedural dialog systems given only light supervision, of the type that can be provided by non-experts. We consider domains where the required task knowledge exists in textual form (e.g., in- structional web pages) and where system builders have access to statements of user intent (e.g., search query logs or dialog interactions). To learn from such tex- tual resources, we describe a novel ap- proach that first automatically extracts task knowledge from instructions, then learns a dialog manag...