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The primary purpose of dialogue state tracking (DST), a critical component ofan end-to-end conversational system, is to build a model that responds well toreal-world situations. Although we often change our minds from time to timeduring ordinary conversations, current benchmark datasets do not adequatelyreflect such occurrences and instead consist of over-simplified conversations,in which no one changes their mind during a conversation. As the main questioninspiring the present study, "Are current benchmark datasets sufficientlydiverse to handle casual conversations in which one changes their mind after acertain topic is over?" We found that the answer is "No" because DST modelscannot refer to previous user preferences when template-based turnbackutterances are injected into the dataset. Even in the the simplestmind-changing (turnback) scenario, the performance of DST models significantlydegenerated. However, we found that this performance degeneration can berecovered when the turnback scenarios are explicitly designed in the trainingset, implying that the problem is not with the DST models but rather with theconstruction of the benchmark dataset.

Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances