# Detecting Agreement in Multi-party Conversational AI

arXiv: https://arxiv.org/abs/2311.03026 · HTML: https://mlsanity.com/paper/2311.03026/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding](https://mlsanity.com/paper/2106.01541/) (2021)
- [Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering](https://mlsanity.com/paper/2308.15231/) (2023)
- [Building for Speech: Designing the Next Generation of Social Robots for Audio Interaction](https://mlsanity.com/paper/2311.01146/) (2023)

## Built on by (most cited first, 1 of 1)

- [Socially Pertinent Robots in Gerontological Healthcare](https://mlsanity.com/paper/2404.07560/) (2024)

## Abstract (arXiv)

Today, conversational systems are expected to handle conversations in multi-party settings, especially within Socially Assistive Robots (SARs). However, practical usability remains difficult as there are additional challenges to overcome, such as speaker recognition, addressee recognition, and complex turn-taking. In this paper, we present our work on a multi-party conversational system, which invites two users to play a trivia quiz game. The system detects users' agreement or disagreement on a final answer and responds accordingly. Our evaluation includes both performance and user assessment results, with a focus on detecting user agreement. Our annotated transcripts and the code for the proposed system have been released open-source on GitHub.