# Community Regularization of Visually-Grounded Dialog

arXiv: https://arxiv.org/abs/1808.04359 · HTML: https://mlsanity.com/paper/1808.04359/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication](https://mlsanity.com/paper/2409.17348/) (2024)
- [Multitasking Inhibits Semantic Drift](https://mlsanity.com/paper/2104.07219/) (2021)
- [Interpretable Learned Emergent Communication for Human-Agent Teams](https://mlsanity.com/paper/2201.07452/) (2022)
- [CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2208.13626/) (2022)
- [SimSiam Naming Game: A Unified Approach for Emergent Communication and Representation Learning](https://mlsanity.com/paper/2410.21803/) (2024)

## Abstract (arXiv)

The task of conducting visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers in natural language. We posit that requiring artificial agents to adhere to the rules of human language, while also requiring them to maximize information exchange through dialog is an ill-posed problem. We observe that humans do not stray from a common language because they are social creatures who live in communities, and have to communicate with many people everyday, so it is far easier to stick to a common language even at the cost of some efficiency loss. Using this as inspiration, we propose and evaluate a multi-agent community-based dialog framework where each agent interacts with, and learns from, multiple agents, and show that this community-enforced regularization results in more relevant and coherent dialog (as judged by human evaluators) without sacrificing task performance (as judged by quantitative metrics).