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

While end-to-end neural machine translation (NMT) has made remarkable
progress recently, it still suffers from the data scarcity problem for
low-resource language pairs and domains. In this paper, we propose a method for
zero-resource NMT by assuming that parallel sentences have close probabilities
of generating a sentence in a third language. Based on this assumption, our
method is able to train a source-to-target NMT model ("student") without
parallel corpora available, guided by an existing pivot-to-target NMT model
("teacher") on a source-pivot parallel corpus. Experimental results show that
the proposed method significantly improves over a baseline pivot-based model by
+3.0 BLEU points across various language pairs.

本文提出了一种基于零资源的神经机器翻译方法，该方法假设平行句子在第三种语言中生成的可能性相近，借助于一种现有的基于枢轴语言的 NMT 模型进行指导，在没有平行语料库的情况下训练源语言到目标语言的 NMT 模型。实验结果表明，该方法在各种语言对上能够显著提高 BLEU 值 3.0 点以上。