"Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue
Anh Ngo, Nicolas Rollet, Catherine Pelachaud, Chloé Clavel
摘要
Maintaining mutual understanding is a key component in human-human conversation to avoid conversation breakdowns, in which repair, particularly Other-Initiated Repair (OIR, when one speaker signals trouble and prompts the other to resolve), plays a vital role. However, Conversational Agents (CAs) still fail to recognize user repair initiation, leading to breakdowns or disengagement. This work proposes a multimodal model to automatically detect repair initiation in Dutch dialogues by integrating linguistic and prosodic features grounded in Conversation Analysis. The results show that prosodic cues complement linguistic features and significantly improve the results of pretrained text and audio embeddings, offering insights into how different features interact. Future directions include incorporating visual cues, exploring multilingual and cross-context corpora to assess the robustness and generalizability.
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- Robust Speech Recognition via Large-Scale Weak SupervisionAlec Radford, Jong Wook Kim, Tao Xu, Greg Brockman 等ICML 2023 · 被引用 6,966 次
- Multi-Modal Repairs of Conversational Breakdowns in Task-Oriented DialogsToby Jia-Jun Li, Jingya Chen, Haijun Xia, Tom M. Mitchell 等UIST 2020 · 被引用 98 次
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- My Bad! Repairing Intelligent Voice Assistant Errors Improves InteractionAndrea Cuadra, Shuran Li, Hansol Lee, Jason Cho 等CSCW 2021 · 被引用 52 次
- Understanding is a Two-Way Street: User-Initiated Repair on Agent Responses and Hearing in Conversational InterfacesRobert J. Moore, Sungeun An, Olivia H. MarreseCSCW 2024 · 被引用 3 次
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