To detect errors, spoken language interfaces typically rely on confidence scores. In this work we investigated in depth current supervised learning techniques for building error detection models. In addition, we proposed a novel, implicitly-supervised approach for this task. No developer supervision is required in this case; rather, the system obtains the supervision signal online, from naturally-occurring patterns in the interaction. We believe this learning paradigm represents an important step towards constructing autonomously self-improving systems. Furthermore, we developed a scalable, data-driven approach that allows a system to continuously monitor and update beliefs throughout the conversation; the proposed approach leads to significant improvements in both the overall effectiveness and efficiency of the interaction. 
