SpeechCycle Confidential Confidential 1 Optimizing Natural Language Interfaces: No Data Like More Data SpeechTEK New York, 2007 Jonathan Bloom & Roberto Pieraccini

SpeechCycle Confidential Confidential 2 SpeechCycle Confidential Just as speech scientists crunch data to optimize a speech recognizer, speech companies need to crunch data to optimize a call at the dialog level as well. Voice user interface (VUI) design needs to be based on quantitative data as much as possible. We will provide an example of quantitative research in the area of VUI design. We will ask, “What is the future of this research-based VUI approach?” Executive Summary

SpeechCycle Confidential Confidential 3 SpeechCycle Confidential VUI = RELIGION + SCIENCE

SpeechCycle Confidential Confidential 4 SpeechCycle Confidential Where there is data, we use it. At those times, it is a science. All else is faith-based. Religion is a fine thing. But our customers do not pay us for theology lessons. They pay us to save them $$$ and keep their customers happy. VUI = RELIGION + SCIENCE

SpeechCycle Confidential Confidential 5 As much as possible, speech companies need to collect data in order to optimize their voice user interfaces at every level – and in every facet - of the interaction, from recognition accuracy to prompt wording to dialog structure. In doing so, VUI will become less anecdotal and more scientific. The Point

SpeechCycle Confidential Confidential 6 One needs the ability to compare two or more versions of an application… …running at the same time. …taking calls from the same population of callers. …taking calls from that population at random. …gathering data at a fast enough pace to meet customer deadlines. Requirements for Exploration

SpeechCycle Confidential Confidential 7 Designing Exploration Alternatives

SpeechCycle Confidential Confidential 8 Example “All of our agents are currently helping other customers. Let’s get started with our automated internet troubleshooter, so you don’t have to wait. [chime] To begin, briefly describe the problem, saying something like “I can’t connect to the internet”, or you can say “What are my choices”. “All of our agents are currently helping other customers. Let’s get started with our automated internet troubleshooter, so you don’t have to wait. [chime] Are you calling because you’ve lost your internet connection? [pause] Please say yes or no.”

SpeechCycle Confidential Confidential 9 Example RESULTS INTRO followed by SLM = 20.4% automation rate INTRO followed by YES/NO question = 22.6% automation rate (CHI square) Statistically significant at.05 level

SpeechCycle Confidential Confidential 10 Summary (Up To Now) Strong opinions and qualitative usability tests should not be the only sources of VUI knowledge. With the right tools, continual access to data, and with enough data, speech companies can make dialog experimentation a regular part of the product lifecycle. At this point, other than the randomization script in the call flow, a lot of this process is manual, so now we need to ask, “How can we make this process more automatic?” What’s next?

SpeechCycle Confidential Confidential 11 Reinforcement Learning theory A finite choice of actions What the system does A finite or infinite set of interaction states Identify the factors that can influence the choice of the best action Policy Choose the action based on current state of the interaction Maximization of a return function Reinforce the choice of the actions that provide a positive return at any particular state The process converges to a locally optimal policy

SpeechCycle Confidential Confidential 12 Reinforcement Learning in dialog research Research has proven that reinforcement learning can be used for automating the design of dialog systems (Markov Decision Processes) E. Levin and R. Pieraccini (AT&T), 1997 (Partially Observable Markov Decision Processes) S. Young (Univ. Cambridge, 2004) Unfortunately, full dialog learning and optimization requires a lot of interactions Academic research uses simulated users Restricting the optimization to a small number of reasonable “competing designs” Alternative competing designs at different points in the application Exploration and Exploitation principle

SpeechCycle Confidential Confidential 13 Conclusions VUI design—from religion to science Use data to validate and chose optimal design among competing alternatives Reinforcement Learning—from theory to practice Use data to optimize applications while they interact