# Emergent Translation in Multi-Agent Communication

arXiv: https://arxiv.org/abs/1710.06922 · HTML: https://mlsanity.com/paper/1710.06922/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2017.

## Built on by (most cited first, 22 of 22)

- [LLM Harmony: Multi-Agent Communication for Problem Solving](https://mlsanity.com/paper/2401.01312/) (2024)
- [Neural Machine Translation with Phrase-Level Universal Visual Representations](https://mlsanity.com/paper/2203.10299/) (2022)
- [Emergent Discrete Communication in Semantic Spaces](https://mlsanity.com/paper/2108.01828/) (2021)
- [Linking Emergent and Natural Languages via Corpus Transfer](https://mlsanity.com/paper/2203.13344/) (2022)
- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Towards More Human-like AI Communication: A Review of Emergent Communication Research](https://mlsanity.com/paper/2308.02541/) (2023)
- [Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication](https://mlsanity.com/paper/2111.06464/) (2021)
- [Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability](https://mlsanity.com/paper/2106.03982/) (2021)
- [Dynamic population-based meta-learning for multi-agent communication with natural language](https://mlsanity.com/paper/2110.14241/) (2021)
- [EC^2: Emergent Communication for Embodied Control](https://mlsanity.com/paper/2304.09448/) (2023)
- [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026)
- [The emergence of visual semantics through communication games](https://mlsanity.com/paper/2101.10253/) (2021)
- [Learning to translate by learning to communicate](https://mlsanity.com/paper/2207.07025/) (2022)
- [Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs](https://mlsanity.com/paper/2402.16713/) (2024)
- [PatchGame: Learning to Signal Mid-level Patches in Referential Games](https://mlsanity.com/paper/2111.01785/) (2021)
- [Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks](https://mlsanity.com/paper/2201.05489/) (2022)
- [Unsupervised Translation of Emergent Communication](https://mlsanity.com/paper/2502.07552/) (2025)
- [Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima](https://mlsanity.com/paper/2303.04544/) (2023)
- [A framework for the emergence and analysis of language in social learning agents](https://mlsanity.com/paper/2305.02632/) (2023)
- [Joining the Conversation: Towards Language Acquisition for Ad Hoc Team Play](https://mlsanity.com/paper/2305.12235/) (2023)
- [GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective](https://mlsanity.com/paper/2507.09495/) (2025)

## Abstract (arXiv)

While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans. In this work, we propose a communication game where two agents, native speakers of their own respective languages, jointly learn to solve a visual referential task. We find that the ability to understand and translate a foreign language emerges as a means to achieve shared goals. The emergent translation is interactive and multimodal, and crucially does not require parallel corpora, but only monolingual, independent text and corresponding images. Our proposed translation model achieves this by grounding the source and target languages into a shared visual modality, and outperforms several baselines on both word-level and sentence-level translation tasks. Furthermore, we show that agents in a multilingual community learn to translate better and faster than in a bilingual communication setting.