Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System

We present a log-linear ranking model for interpreting questions in a virtual patient dialogue system and demonstrate that it substantially outperforms a more typical multiclass classifier model using the same information. The full model makes use of weighted and concept-based matching features that together yield a 15% error reduction over a strong lexical overlap baseline. The accuracy of the ranking model approaches that of an extensively handcrafted pattern matching system, promising to reduce the authoring burden and make it possible to use confidence estimation in choosing dialogue acts; at the same time, the effectiveness of the concept-based features indicates that manual development resources can be productively employed with the approach in developing concept hierarchies.