Dialogue System Characterisation by Back-channelling Patterns Extracted from Dialogue Corpus
In this study, we describe the use of back-channelling patterns extracted from a dialogue corpus as a mean to characterising text-based dialogue systems. Our goal was to provide system users with the feeling that they are interacting with distinct individuals rather than artificially created characters. An analysis of the corpus revealed that substantial difference exists among speakers regarding the usage patterns of back-channelling. The patterns consist of back-channelling frequency, types, and expressions. They were used for system characterisation. Implemented system characters were tested by asking users of the dialogue system to identify the source speakers in the corpus. Experimental results suggest that possibility of using back-channelling patterns alone to characterize the dialogue system in some cases even among the same age and gender groups.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Robotic Backchanneling in Online Conversation Facilitation: A Cross-Generational Study
Japan faces many challenges related to its aging society, including increasing rates of cognitive decline in the population and a shortage of caregivers. Efforts have begun to explore solutions using artificial intellige…
Bias Beneath the Tone: Empirical Characterisation of Tone Bias in LLM-Driven UX Systems
Large Language Models are increasingly used in conversational systems such as digital personal assistants, shaping how people interact with technology through language. While their responses often sound fluent and natura…
Emotion RecognitionChannelling, Coordinating, Collaborating: A Three-Layer Framework for Disability-Centered Human-Agent Collaboration
AI accessibility tools have mostly been designed for individual use, helping one person overcome a specific functional barrier. But for many people with disabilities, complex tasks are accomplished through collaboration …
How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction
Heuristic and scaffolded teacher-student dialogues are widely regarded as critical for fostering students' higher-order thinking and deep learning. However, large language models (LLMs) currently face challenges in gener…
QRFA: A Data-Driven Model of Information-Seeking Dialogues
Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances ex…