# Multi-agent Learning for Neural Machine Translation

arXiv: https://arxiv.org/abs/1909.01101 · HTML: https://mlsanity.com/paper/1909.01101/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

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

- [Exploring the Intersection of Large Language Models and Agent-Based Modeling via Prompt Engineering](https://mlsanity.com/paper/2308.07411/) (2023)
- [Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation](https://mlsanity.com/paper/2106.06751/) (2021)
- [Improved Training of Mixture-of-Experts Language GANs](https://mlsanity.com/paper/2302.11875/) (2023)
- [Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play](https://mlsanity.com/paper/2410.18935/) (2024)
- [Translation as a Decision Space: A Multi-Agent Perspective on Low-Resource Dialect Generation](https://mlsanity.com/paper/2609.04048/) (2026)

## Abstract (arXiv)

Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e.g., dual learning, and bidirectional decoding with one agent decoding from left to right and the other decoding in the opposite direction. In this paper, we extend the training framework to the multi-agent scenario by introducing diverse agents in an interactive updating process. At training time, each agent learns advanced knowledge from others, and they work together to improve translation quality. Experimental results on NIST Chinese-English, IWSLT 2014 German-English, WMT 2014 English-German and large-scale Chinese-English translation tasks indicate that our approach achieves absolute improvements over the strong baseline systems and shows competitive performance on all tasks.