NLP@Pitt

[news] Congratulations to Greg Nicholas

Greg received an Honorable Mention for CRA's Outstanding Undergraduate Award for 2007!

Measuring Lexical and Acoustic/Prosodic Priming in Dialogs

Speaker: Art Ward Experimental research has shown that human users will converge with dialog systems along many dimensions of speech, including those of acoustic/prosodic features and lexical choice. Other results suggest that speech convergence may provide a variety of benefits to spoken dialog systems, such as increased ease of use, improved feelings of intimacy, and increased compliance on the part of the user. These potential benefits to dialog systems of detecting or generating convergence behaviors suggest the need for corpus studies of convergence, in addition to the experimental results. Here, we build on previous work to demonstrate corpus measures of lexical and acoustic/prosodic priming. We show that these measures successfully distinguish randomized from naturally ordered data, and demonstrate both lexical and acoustic/prosodic priming effects in our corpus of human/human tutoring dialogs.

How much data is enough? (Experiments with Confidence Bounds for MDP's)

Speaker: Joel Tetreault Data sparsity is one of the major issues that NLP researchers always wrestle with. That is, does one have enough data to make reliable conclusions in an experiment? Using Reinforcement Learning to improve a spoken dialogue system is no exception. Past approaches in this area have simply assumed that there was enough collected data to support a certain state and action space, or used thousands of user simulations to overcome the sparsity issue. In this talk, we present a methodology of confidence bounds on the expected reward to address the problem of data sparsity in MDP's. We show how this methodology works by apply it to a prior experiment of using MDP's to predict the best features to include in a model of the dialogue state. We also show how this approach has applications in model switching and user simulations.