  Intelligent assistants (IAs) such as Siri and Cortana conversationally
interact with users and execute a wide range of actions (e.g., searching the
Web, setting alarms, and chatting). IAs can support these actions through the
combination of various components such as automatic speech recognition, natural
language understanding, and language generation. However, the complexity of
these components hinders developers from determining which component causes an
error. To remove this hindrance, we focus on reformulation, which is a useful
signal of user dissatisfaction, and propose a method to predict the
reformulation causes. We evaluate the method using the user logs of a
commercial IA. The experimental results have demonstrated that features
designed to detect the error of a specific component improve the performance of
reformulation cause detection.
