While end-to-end neural machine translation (NMT) has achieved notable
success in the past years in translating a handful of resource-rich language
pairs, it still suffers from the data scarcity problem for low-resource
language pairs and domains. To tackle this problem, we propose an interactive
multimodal framework for zero-resource neural machine translation. Instead of
being passively exposed to large amounts of parallel corpora, our learners
(implemented as encoder-decoder architecture) engage in cooperative image
description games, and thus develop their own image captioning or neural
machine translation model from the need to communicate in order to succeed at
the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that
the proposed learning mechanism significantly improves over the
state-of-the-art methods.

本文提出了一种交互式多模态框架，通过游戏的方式解决了低资源语言对和领域下数据匮乏的翻译问题，并在实验中证明此学习机制显著优于现有的最先进方法。

基于多智能体通信协作的零资源神经机器翻译算法

Zero-Resource Neural Machine Translation with Multi-Agent Communication  Game

We propose an interactive multimodal framework for language learning. Instead
of being passively exposed to large amounts of natural text, our learners
(implemented as feed-forward neural networks) engage in cooperative referential
games starting from a tabula rasa setup, and thus develop their own language
from the need to communicate in order to succeed at the game. Preliminary
experiments provide promising results, but also suggest that it is important to
ensure that agents trained in this way do not develop an adhoc communication
code only effective for the game they are playing

提出了一种交互式多模态框架，通过协作推理游戏，实现神经网络学习语言，初步实验结果可喜，但需注意避免智能体发展出仅针对游戏有效的特定通信编码。