  Gender-neutral pronouns are increasingly being introduced across Western
languages. Recent evaluations have however demonstrated that English NLP
systems are unable to correctly process gender-neutral pronouns, with the risk
of erasing and misgendering non-binary individuals. This paper examines a Dutch
coreference resolution system's performance on gender-neutral pronouns,
specifically hen and die. In Dutch, these pronouns were only introduced in
2016, compared to the longstanding existence of singular they in English. We
additionally compare two debiasing techniques for coreference resolution
systems in non-binary contexts: Counterfactual Data Augmentation (CDA) and
delexicalisation. Moreover, because pronoun performance can be hard to
interpret from a general evaluation metric like LEA, we introduce an innovative
evaluation metric, the pronoun score, which directly represents the portion of
correctly processed pronouns. Our results reveal diminished performance on
gender-neutral pronouns compared to gendered counterparts. Nevertheless,
although delexicalisation fails to yield improvements, CDA substantially
reduces the performance gap between gendered and gender-neutral pronouns. We
further show that CDA remains effective in low-resource settings, in which a
limited set of debiasing documents is used. This efficacy extends to previously
unseen neopronouns, which are currently infrequently used but may gain
popularity in the future, underscoring the viability of effective debiasing
with minimal resources and low computational costs.
