Abstract
Spoken dialog systems present a classic example of planning under uncertainty. Speech recognition errors are ubiquitous and impossible to detect reliably, so the state of the conversation can never be known with certainty. Despite this, the system must choose actions to make progress to a long term goal. As such, decision theory, and in particular partially-observable Markov decision processes (POMDPs), present an attractive approach to building spoken dialog systems. Initial work on “toy” dialog systems validated the benefits of the POMDP approach; however, it also found that straightforward application of POMDPs could not scale to real-world problems. Subsequent work by a number of research teams has scaled up planning and belief monitoring, incorporated high-fidelity user simulations, and married commercial development practices with automatic optimization. Today, statistical dialog systems are being fielded by research labs for public use. This chapter traces the history of POMDP-based spoken dialog systems, and sketches avenues for future work.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
