  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.
