Abstract
There is an ongoing discussion on what makes humans more engaged when interacting with conversational agents. However, in the area of language processing, there has been a paucity of studies on how people react to agents and share interactions with others. We attack this issue by investigating the user dialogues with human-like agents posted online and aim to analyze the dialogue patterns. We construct a taxonomy to discern the users’ self-disclosure in the dialogue and the communication authenticity displayed in the user posting. We annotate the in-the-wild data, examine the reliability of the proposed scheme, and discuss how the categorization can be utilized for future research and industrial development.- Anthology ID:
- 2022.findings-aacl.14
- Volume:
- Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 145–152
- Language:
- URL:
- https://aclanthology.org/2022.findings-aacl.14
- DOI:
- Cite (ACL):
- Won Ik Cho, Soomin Kim, Eujeong Choi, and Younghoon Jeong. 2022. Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 145–152, Online only. Association for Computational Linguistics.
- Cite (Informal):
- Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee (Cho et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.findings-aacl.14.pdf