Reflective action selection based on positive-unlabeled learning and causality detection model. (March 2023) - Tanaka, Shohei; Yoshino, Koichiro; Sudoh, Katsuhito; Nakamura, Satoshi
Abstract: Task-oriented dialogue systems need to take appropriate actions not only for clear user requests but also for ambiguous and vague ones. In this study, "ambiguous" denotes that although users have potential requests, they failed to clearly define and verbalize their content and conditions which can be associated with system actions. For such ambiguous requests, taking reflective actions is one plausible choice for such systems. In our study, "reflective" denotes taking actions that satisfy user requests before the users themselves clarify their demands. We constructed such a reflective dialogue agent by collecting a corpus that includes pairs of ambiguous user requests and corresponding reflective system actions on sightseeing navigation with a smartphone. Since annotating every possible combination of user requests and system actions is impossible, this study built a corpus where one reflective action is annotated to one ambiguous user request. To train an action selection model on such incomplete training data in which only one action is associated with a request, we applied the positive/unlabeled (PU) learning method, which assumes that only part of the data is labeled with positive examples. In addition, we enhanced the action selection by extracting and distilling knowledge that corresponds to causality from the training data using a causality detection model. The experimental results show that both the PU learning method and the causality detection model improved the performances of the reflective action selection compared to the conventional positive/negative (PN) learning method. Highlights: We built a reflective action selection system given ambiguous user requests. We collected a dialogue corpus that bridges ambiguous requests to reflective actions. We explored whether several actions can be reflective for one request on test data. We applied the PU learning method to deal with partially annotated training data. We distilled causality knowledge to utilize in the reflective action selection.