We formulate and test a technique to use Emergent Communication (EC) with a pretrained multilingual model to improve on modern Unsupervised NMT systems, especially for low-resource languages. It has been argued that the currently dominant paradigm in NLP of pretraining on text-only corpora will not yield robust natural language understanding systems, and the need for grounded, goal-oriented, and interactive language learning has been highlighted. In our approach, we embed a modern multilingual model (mBART, Liu et. al. 2020) into an EC image-reference game, in which the model is incentivized to use multilingual generations to accomplish a vision-grounded task, with the hypothesis that this will align multiple languages to a shared task space. We present two variants of EC Fine-Tuning (Steinert-Threlkeld et. al. 2022), one of which outperforms a backtranslation-based baseline in 6/8 translation settings, and proves especially beneficial for the very low-resource languages of Nepali and Sinhala.

本文中，我们提出并测试了一种使用Emergent Communication技术，以先前预训练好的多语言模型来改进现代无监督机器翻译系统的方法，特别是对于语料库数据量很小的语言，我们将现代多语言模型嵌入到了一个以视觉为基础的语言游戏中，并通过一个共同目标的任务去训练模型，证明此方法能够提高翻译表现，特别是对波斯语和僧伽罗语这样语言数据量很小的语言有很大的帮助。

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