  Training statistical dialog models in spoken dialog systems (SDS) requires
large amounts of annotated data. The lack of scalable methods for data mining
and annotation poses a significant hurdle for state-of-the-art statistical
dialog managers. This paper presents an approach that directly leverage
billions of web search and browse sessions to overcome this hurdle. The key
insight is that task completion through web search and browse sessions is (a)
predictable and (b) generalizes to spoken dialog task completion. The new
method automatically mines behavioral search and browse patterns from web logs
and translates them into spoken dialog models. We experiment with naturally
occurring spoken dialogs and large scale web logs. Our session-based models
outperform the state-of-the-art method for entity extraction task in SDS. We
also achieve better performance for both entity and relation extraction on web
search queries when compared with nontrivial baselines.
