Responsible development of technology involves applications being inclusive
of the diverse set of users they hope to support. An important part of this is
understanding the many ways to refer to a person and being able to fluently
change between the different forms as needed. We perform a case study on the
singular they, a common way to promote gender inclusion in English. We define a
re-writing task, create an evaluation benchmark, and show how a model can be
trained to produce gender-neutral English with <1% word error rate with no
human-labeled data. We discuss the practical applications and ethical
considerations of the task, providing direction for future work into inclusive
natural language systems.

本研究关注技术的负责任发展，着眼于包容其希望支持的不同用户群体。在此基础上，对英文中一种常见的提倡性别包容的表达方式 —— 单数 they 展开案例研究。研究通过定义重写任务，创建评估基准，构建模型等方式，成功实现了语言性别中立，无需人工标记数据即可达到小于 1% 的错误率。此外，研究探讨了该任务的实际应用和伦理考虑，并提出了未来研究的方向，旨在推动包容性自然语言系统的发展。

他们、他们的：使用中性英语重写

They, Them, Theirs: Rewriting with Gender-Neutral English

The current mainstream approach to train natural language systems is to
expose them to large amounts of text. This passive learning is problematic if
we are interested in developing interactive machines, such as conversational
agents. We propose a framework for language learning that relies on multi-agent
communication. We study this learning in the context of referential games. In
these games, a sender and a receiver see a pair of images. The sender is told
one of them is the target and is allowed to send a message from a fixed,
arbitrary vocabulary to the receiver. The receiver must rely on this message to
identify the target. Thus, the agents develop their own language interactively
out of the need to communicate. We show that two networks with simple
configurations are able to learn to coordinate in the referential game. We
further explore how to make changes to the game environment to cause the "word
meanings" induced in the game to better reflect intuitive semantic properties
of the images. In addition, we present a simple strategy for grounding the
agents' code into natural language. Both of these are necessary steps towards
developing machines that are able to communicate with humans productively.

本文提出了一种基于多智能体交互通信的语言学习框架，在指代游戏的上下文中研究了这种学习方法，通过联系人工智能代理人彼此通信以识别随机图像，在通过调整游戏环境来提高代理人交流的自适应语言结构，并为代理人的代码提出简单的系统，从而使其更好地恰当地通信，并能更有效地与人类沟通。