Learning the structure of human-computer and human-human dialogs
David Griol, Giuseppe Riccardi, Emilio Sanchis · 2009
We are interested in the problem of understanding human conversation structure in the context of human-machine and human-human interaction. We present a statistical methodol-ogy for detecting the structure of spoken dialogs based on a generative model learned using decision trees. To evaluate our approach we have used the LUNA corpora, collected from real users engaged in problem solving tasks. The results of the evaluation show that automatic segmentation of spoken dialogs is very effective not only with models built using separately human-machine dialogs or human-human dialogs, but it is also possible to infer the task-related structure of human-human di-alogs with a model learned using only human-machine dialogs.