# Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner

arXiv: https://arxiv.org/abs/1808.07104 · HTML: https://mlsanity.com/paper/1808.07104/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [The Authenticity Gap in Human Evaluation](https://mlsanity.com/paper/2205.11930/) (2022)
- [PRODIGy: a PROfile-based DIalogue Generation dataset](https://mlsanity.com/paper/2311.05195/) (2023)
- [Leveraging Implicit Feedback from Deployment Data in Dialogue](https://mlsanity.com/paper/2307.14117/) (2023)
- [EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments](https://mlsanity.com/paper/2111.00310/) (2021)
- [Evaluating and Improving Context Attention Distribution on Multi-Turn Response Generation using Self-Contained Distractions](https://mlsanity.com/paper/2211.04943/) (2022)
- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)

## 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.