学习猜猜谁？发明一种扎根的语言作为一种结果
Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
摘要：
掌握你的第一语言是一个令人难以置信的壮举，不容易复制。仅仅依靠几本没有图片的书，一个语料库，就能学会交流，这对人类来说也是不可能的。然而，这是当今大多数自然语言处理中的主导方法。作为替代方案，我们建议使用代理之间的位置交互作为通信的驱动力，以及深度递归Q网络的框架，用于在所提供的环境中发展共享语言。我们的任务与互动的图像搜索的形式的游戏猜猜谁代理？游戏中的图像为代理人提供了一个非平凡的环境来讨论，并为他们决定在通信中编码的概念提供了自然的基础。我们的实验表明，代理人不仅学会编码物理概念，在他们的话，即接地，但代理人也学会举行一个多步骤的对话，记住对话的状态，从一步到一步。
英文摘要：
Acquiring your first language is an incredible feat and not easily duplicated. Learning to communicate using nothing but a few pictureless books, a corpus, would likely be impossible even for humans. Nevertheless, this is the dominating approach in most natural language processing today. As an alternative, we propose the use of situated interactions between agents as a driving force for communication, and the framework of Deep Recurrent Q-Networks for evolving a shared language grounded in the provided environment. We task the agents with interactive image search in the form of the game Guess Who?. The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication. Our experiments show that the agents learn not only to encode physical concepts in their words, i.e. grounding, but also that the agents learn to hold a multi-step dialogue remembering the state of the dialogue from step to step.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
