Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner
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Abstract (arXiv)
There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Aug 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 1.
7 papers.
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- 2022Evaluating and Improving Context Attention Distribution on Multi-Turn Response Generation using Self-Contained Distractionscited by 1
- 2024Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Surveycited by 1
- 2024Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey14 Nov 2024
- 2023PRODIGy: a PROfile-based DIalogue Generation dataset9 Nov 2023
- 2023Leveraging Implicit Feedback from Deployment Data in Dialogue26 Jul 2023
- 2022Evaluating and Improving Context Attention Distribution on Multi-Turn Response Generation using Self-Contained Distractions9 Nov 2022
- 2022The Authenticity Gap in Human Evaluation24 May 2022
- 2022Survey of Hallucination in Natural Language Generation8 Feb 2022
- 2021EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments30 Oct 2021