Lightly Supervised Learning of Procedural Dialog Systems

Svitlana Volkova, Pallavi Choudhury, Chris Quirk, Bill Dolan, Luke S. Zettlemoyer

Abstract: 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 knowledge and dialog management strategies. In this paper, we demonstrate that it is possible 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., instructional 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 textual resources, we describe a novel approach that first automatically extracts task knowledge from instructions, then learns a dialog manager over this task knowledge to provide assistance. Evaluation in a Microsoft Office domain shows that the individual components are highly accurate and can be integrated into a dialog system that provides effective help to users.