Learning dialogue management models poses significant challenges. In a complex taskoriented domain in which information is exchanged via parallel, interleaved dialogue and task streams, effective dialogue management models should be able to make dialogue moves based on both the dialogue and the task context. This paper presents a data-driven approach to learning dialogue management models that determine when to make dialogue moves to assist users’ task completion activities, as well as the type of dialogue move that should be selected for a given user interaction context. Combining features automatically extracted from the dialogue and the task, we compare two alternate modeling approaches. The results of an evaluation indicate the learned models are effective in predicting both the timing and the type of system dialogue moves.
['Eun Ha', 'Christopher Michael Mitchell', 'Kristy Elizabeth Boyer', 'James C. Lester']
Learning Dialogue Management Models for Task-Oriented Dialogue with Parallel Dialogue and Task Streams