"Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909873983193088 |
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| author | Ngo, Anh Rollet, Nicolas Pelachaud, Catherine Clavel, Chloe |
| author_facet | Ngo, Anh Rollet, Nicolas Pelachaud, Catherine Clavel, Chloe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | "Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue Ngo, Anh Rollet, Nicolas Pelachaud, Catherine Clavel, Chloe Computation and Language 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. |
| title | "Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.24628 |