Contender
David Suendermann, Jackson Liscombe, Roberto Pieraccini · 2010
Contender (or what the academic community would refer to as a light version of reinforcement learning) is a simple technique to experiment with a number of competing paths in a (commercial) spoken dialog system. By randomly routing certain portions of traffic to individual paths and computing average rewards for each of the routes, the goal is to find out which one performs best. This paper is to do away with common uncertainties on how to set up contender weights, how much data needs to be accumulated to draw reliable conclusions, and how this all relates to the notion of statistical significance.