TY - GEN
T1 - "Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue
AU - Ngo, Anh
AU - Rollet, Nicolas
AU - Pelachaud, Catherine
AU - Clavel, Chloé
N1 - Publisher Copyright:
© 2025 Association for Computational Linguistics.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105040243918
U2 - 10.18653/v1/2025.emnlp-main.1168
DO - 10.18653/v1/2025.emnlp-main.1168
M3 - Conference contribution
AN - SCOPUS:105040243918
T3 - EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
SP - 22926
EP - 22939
BT - EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
A2 - Christodoulopoulos, Christos
A2 - Chakraborty, Tanmoy
A2 - Rose, Carolyn
A2 - Peng, Violet
PB - Association for Computational Linguistics (ACL)
T2 - 30th Conference on Empirical Methods in Natural Language Processing, EMNLP 2025
Y2 - 4 November 2025 through 9 November 2025
ER -