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

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...