SCAINs Presenter: Preventing Miscommunication by Detecting Context-Dependent Utterances in Spoken Dialogue

When individuals are talking while performing multiple tasks at the same time, it is sometimes easy to miss parts of a conversation and misinterpret subsequent statements or have difficulty following the conversation. In this work, we aim to identify statements that may lead to misinterpretation of the subsequent statement if missed and to prevent communication discrepancies. Although there have been several attempts to present images and text that provide topics to support conversation, there is currently no system that supports conversation by taking interpretability into account. We propose a conversation support system SCAINs Presenter that presents Statements Crucial for Awareness of Interpretive Nonsense (SCAINs), which are statements that are important for interpreting other sentences and are extracted by reproducing the interpretations of those who missed part of the conversation and those who did not. The unique point of the SCAINs Presenter is to display extracted sentences that influence the context of the subsequent dialogue by taking into account their interpretability. In particular, since SCAINs are sentences that may cause misinterpretation of the subsequent dialogue if they are absent, the SCAINs Presenter helps the users to be aware of the possibility of a conversation gap coming from the misinterpretation. Our experiments show that when SCAINs are omitted, the intention of the following statements often becomes unclear, and the meaning of the following statements changes. We also found that SCAINs can capture a unique aspect different from the merely important statements. Moreover, the results of case studies in a realistic setting suggest that looking at SCAINs encourages conversation participants to switch their focus from a subtask chat to an ongoing conversation that is a primary task. Our research clarifies the linguistic processing underlying the identification of high-context utterances and demonstrates the effectiveness of using them to support real person-to-person interactions.