"Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
An Analysis of Dialogue Repair in Voice Assistants
Spoken dialogue systems have transformed human-machine interaction by providing real-time responses to queries. However, misunderstandings between the user and system persist. This study explores the significance of inte…
Spoken Dialogue SystemsUnified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues
Spoken dialogue systems (SDSs) have been separately developed under two different categories, task-oriented and chit-chat. The former focuses on achieving functional goals and the latter aims at creating engaging social …
SentenceSpoken Dialogue SystemsA data-driven model of explanations for a chatbot that helps to practice conversation in a foreign language
This article describes a model of other-initiated self-repair for a chatbot that helps to practice conversation in a foreign language. The model was developed using a corpus of instant messaging conversations between Ger…
ChatbotLanguage AcquisitionRetrievalTalking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs
Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we investigate how LLMs engage in the interactive process of repair in multi…
User-initiated Sub-dialogues in State-of-the-art Dialogue Systems
We test state of the art dialogue systems for their behaviour in response to user-initiated sub-dialogues, i.e. interactions where a system question is responded to with a question or request from the user, who thus init…
Dialogue ManagementSpoken Dialogue Systems