# What about Zir? On Pronouns in Machine Translation

Anne Lauscher1, Debora Nozza2, Archie Crowley3, Ehm Miltersen4, and Dirk Hovy2
1Data Science Group, Universität Hamburg, Germany
2Department of Computing Sciences, Bocconi University, Italy
3Linguistics, University of South Carolina
4School of Culture and Communication, Aarhus University
<EMAIL_ADDRESS>{debora.nozza<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
# What about Zir?
How Commercial Machine Translation Systems Fail to Handle Pronouns

Anne Lauscher1, Debora Nozza2, Archie Crowley3, Ehm Miltersen4, and Dirk Hovy2
1Data Science Group, Universität Hamburg, Germany
2Department of Computing Sciences, Bocconi University, Italy
3Linguistics, University of South Carolina
4School of Culture and Communication, Aarhus University
<EMAIL_ADDRESS>{debora.nozza<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
# What about _Zir_?
How Commercial Machine Translation Fails to Handle Pronouns

Anne Lauscher1, Debora Nozza2, Archie Crowley3, Ehm Miltersen4, and Dirk Hovy2
1Data Science Group, Universität Hamburg, Germany
2Department of Computing Sciences, Bocconi University, Italy
3Linguistics, University of South Carolina
4School of Culture and Communication, Aarhus University
<EMAIL_ADDRESS>{debora.nozza<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
# What about _em_?
How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

Anne Lauscher1, Debora Nozza2, Archie Crowley3, Ehm Miltersen4, and Dirk Hovy2
1Data Science Group, Universität Hamburg, Germany
2Department of Computing Sciences, Bocconi University, Italy
3Linguistics, University of South Carolina
4School of Culture and Communication, Aarhus University
<EMAIL_ADDRESS>{debora.nozza<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns,
we need more research on identity-inclusive NLP. Exclusion is particularly
harmful in one of the most popular NLP applications, machine translation (MT).
Wrong pronoun translations can discriminate against marginalized groups, e.g.,
non-binary individuals  (Dev et al., 2021). In this “reality check”, we study
how three commercial MT systems translate 3rd-person pronouns. Concretely, we
compare the translations of gendered vs. gender-neutral pronouns from English
to five other languages (Danish, Farsi, French, German, Italian), and vice
versa, from Danish to English. Our error analysis shows that the presence of a
gender-neutral pronoun often leads to grammatical and semantic translation
errors. Similarly, gender neutrality is often not preserved. By surveying the
opinions of affected native speakers from diverse languages, we provide
recommendations to address the issue in future MT research.

##  1 Introduction

Machine translation (MT) is one of the most common applications of NLP, with
millions of daily users interacting with popular commercial providers (e.g.,
Bing, DeepL, or Google Translate). Given MT’s widespread use and the increased
focus on fairness in language technologies (e.g., Hovy and Spruit, 2016;
Blodgett et al., 2020), previous work has pointed to the potential ethical
issues stemming from stereotypical biases encoded in the models, e.g., gender
or age bias (e.g., Stanovsky et al., 2019; Levy et al., 2021, _inter alia_).

Still, these studies treat gender as a binary variable and ignore the larger
spectrum of (possibly marginalized) identities, e.g., non-binary individuals.
This gender exclusivity stands in stark contrast to the findings of Dev et al.
(2021). Their survey of queer individuals showed that MT has the most
potential for representational and allocational harms (Barocas et al., 2017)
for non-cis users (compared to other NLP applications). In this context,
survey respondents mentioned the translation of _pronouns_ as particularly
sensitive, as gender-neutral pronouns might be translated into gendered
pronouns, resulting in harmful misgendering.

While individual studies have investigated the translation of established
(gender-neutral) pronouns (e.g., from Korean to English; Cho et al., 2019),
NLP research, in general, has ignored the _“modern world of pronouns”_ as
recently described by Lauscher et al. (2022). They discuss the large variety
of existing phenomena in English 3rd-person pronoun usage, with more
traditional neopronoun sets (e.g., _xe/xem_)111Throughout this work, we use
the expression “traditional neopronoun” to refer to sets that are, in contrast
to only recently described phenomena (e.g., nounself pronouns), already
academically discussed for longer. and novel pronoun-related phenomena (e.g.,
nounself pronouns like _vamp/vamp_ ; Miltersen, 2016), which possibly match
distinct aspects of an individuals identity.

As an example of ubiquitous NLP technology, truly inclusive MT should account
for linguistic varieties that express identity aspects, like the large
spectrum of pronouns related to the social push to respect diverse identities.
However, until now, (a) there has been no information on how our systems (fail
to) handle this linguistic shift, and (b) it is unclear how MT should deal
with novel pronouns. This case is especially challenging when source language
pronouns do not have direct correspondences in the target language.

Contributions. In this “reality check”, we investigate the handling of various
(neo)pronouns in MT for advancing inclusive NLP. To this end, we combine an
extensive analysis of MT performance across six languages (Danish, English,
Farsi, French, German, and Italian) and three commercial MT engines (Bing,
DeepL, and Google Translate) with results from the largest survey on pronoun
usage among queer individuals in AI to date. We answer the following four
research questions (RQs):

(RQ1) _How do gender-neutral pronouns affect the overall translation quality?_
We show that compared to gendered pronouns, the translated output’s
grammaticality and the source sequence’s semantic consistency drops by up to
16 percentage points and 47 percentage points, respectively, for some
categories of neopronouns.

(RQ2) _How do MT engines handle gender-neutral pronouns?_ We demonstrate that
the strategies for how MT engines handle pronouns vary by pronoun category:
while gendered pronouns are most often translated (89%), engines tend to
simply copy some categories of neopronouns (e.g., 74% for the category of
numberself-pronouns).

(RQ3) _Which MT strategies for handling gender-neutral pronouns “work”?_ We
show that in 56% of cases when a traditional neopronoun is translated, it is
translated to a gendered pronoun in the target language, likely leading to
misgendering.

(RQ4) _How should MT handle pronouns?_ The answers of 49 participants (149
participants in the pre-study) in our survey reflect the diversity of pronoun
choices across English and other languages and the diversity of preferences in
how individuals’ pronouns should be handled. There is no clear consensus! We
thus recommend providing configuration options to adjust the treatment of
pronouns to individuals’ needs.

##  2 Related Work

We review works on gender bias in MT and the broader area of (gender) identity
inclusion in NLP. For a thorough survey on gender bias in MT, we refer to
(Savoldi et al., 2021).

#### Gender Bias in MT.

As with other areas of NLP (e.g., Bolukbasi et al., 2016; Gonen and Goldberg,
2019; Lauscher et al., 2020; Barikeri et al., 2021, _inter alia_), much
research has been conducted on assessing (binary) gender bias in MT. Most
prominently, Stanovsky et al. (2019) presented the WinoMT corpus, which allows
for assessing occupational gender bias as an extension of Winogender (Rudinger
et al., 2018) and WinoBias (Zhao et al., 2018). Troles and Schmid (2021)
further extended WinoMT with gender-biased verbs and adjectives. Those corpora
are template-based, while Levy et al. (2021) focused on collecting natural
data, and Gonen and Webster (2020) proposed an automatic approach to detect
gender issues in real-world input. Renduchintala et al. (2021) analyzed the
effect of efficiency optimization on the measurable gender bias. Focusing on a
different perspective, Hovy et al. (2020) assessed stylistic (gender) bias in
translations. Other studies have examined specific language pairs, e.g.,
English and Hindi (Ramesh et al., 2021), English and Italian (Vanmassenhove
and Monti, 2021), or English and Turkish (Ciora et al., 2021). Similarly, Cho
et al. (2019) studied English–Korean translations focusing on translating
gender-neutral pronouns from Korean. They introduced a measure reflecting the
preservation of gender neutrality but do not consider any neopronouns. Based
on similar data sets and measures, researchers have also addressed gender bias
in MT, e.g., via domain adaptation (Saunders and Byrne, 2020), debiasing
representations (Escudé Font and Costa-jussà, 2019), adding contextual
information (Basta et al., 2020), and training on gender-balanced corpora
(Costa-jussà and de Jorge, 2020). Some mitigation approaches exploit explicit
gender annotations to guide the model in choosing the intended gender (e.g.,
Stafanovičs et al., 2020). In this context, Saunders et al. (2020) proposed a
schema for adding inflection tags. For instance, they demonstrated how gender-
neutral entities can be translated from English to another language by using a
non-binary inflection tag.

#### Gender and Identity-Inclusion in NLP.

While most MT studies on gender bias deal with a binary notion of gender,
researchers have started to study non-binary gender and identity inclusivity
in NLP downstream tasks and models. Qian et al. (2022) explored the robustness
of models to demographic change using a perturber model that also considers
non-binary gender identities, Cao and Daumé III (2020) studied gender
inclusion in co-reference resolution, and Brandl et al. (2022) analyzed how
gender-neutral pronouns are handled by language models in Danish, English, and
Swedish for natural language inference and co-reference resolution. Nozza et
al. (2022) and Holtermann et al. (2022) measured bias and harmfulness in
language models towards LGBTQIA+ individuals. Other researchers focused on the
problem more broadly. Orgad and Belinkov (2022) mention the binary treatment
of gender as one of the essential pitfalls in gender bias evaluation, and Dev
et al. (2021) surveyed the harms arising from non-binary exclusion in NLP,
indicating MT as one particularly harmful application. Following up, Lauscher
et al. (2022) explored the various phenomena related to 3rd-person pronoun
usage in English, e.g., neopronouns. We are the first to study the translation
of these novel pronoun-related phenomena in MT.

##  3 The Status Quo

To shed light on the state of identity inclusion through 3rd person pronouns
in commercial MT, we conduct a thorough error analysis when translating from
English (en) to five diverse languages. We further describe an experiment
opposite to this, translating from Danish (da) to en, in §3.3.

###  3.1 Experimental Setup

Our overall setup consists of 3 steps: (1) we create en source sentences, each
of which contains 3rd person pronouns representing different “pronoun
categories” (e.g., _gendered pronoun_ , etc.) in different grammatical cases.
(2) Next, we employ an MT system to translate the en sentences to five target
languages. (3) Last, we let native speakers manually analyze the translations
with respect to diverse criteria, e.g., _grammaticality of the output_.

#### Creation of en Source Data.

Case | Templates
---|---
Nominative | _The attendant recognized the developer because <n> reads a lot of technical news._
_The analyst employed the housekeeper because <n> could not stand housework._
_Olivia lost the game, so <n> was sad._
_Liam received a good grade, so <n> was happy._
Accusative | _The developer wanted free bread from the baker and made up a story for <a> about not having a kitchen._
_The attendant did not want to fight with the guard and gave <a> flowers._
_I like Olivia, so I met <a> today._
_I do not like Liam, so I do not want to meet <a> today._
Poss. Depen. | _The mechanic visited the writer and helped on fixing <pd> car engine._
_The baker sold bread to the CEO and enjoyed <pd> visits._
_Liam lost <pd> phone._
_Olivia found <pd> ring._
Poss. Indep. | _During lunch, the janitor looked for the attendant to steal <pi>._
_Last Saturday, the physician called the tailor to fix <pi>._
_I had no phone, so Olivia gave me <pi>._
_I lost my notes, so Liam gave me <pi>._
Reflexive | _The farmer did not want to talk to the writer because <n> was burying <r> in writing a new novel._
_The chief employed the receptionist because <n> was too busy to answer those
phone calls by <r> every day._
_Olivia wanted to impress, so <n> baked a cake <r>._
_Liam wanted a new haircut, so <n> cut the hair <r>._
Table 1: The templates we use for each grammatical case. Placeholders are
indicated with brackets and the grammatical case of the pronoun to fill, e.g.,
_< pd>_ (possessive dependent pronoun). The first two templates for each case
are extracted from WinoMT (Stanovsky et al., 2019), while the second two
templates are added by us.

We start with the WinoMT data set (Stanovsky et al., 2019), designed to assess
gender bias in MT and consisting of sentences that contain occupations
stereotypically associated with women (e.g., _secretary_) or men (e.g.,
_developer_). We conduct an automatic morphological analysis on each pronoun
in the data set.222For this, we use part-of-speech tags and morphological
output generated by spaCy (https://spacy.io). Based on the output, we randomly
sample for each grammatical case (e.g., nominative, etc.), in which a 3rd
person pronoun referring to an occupation appears in, two sentences: one in
which the target occupation is stereotypically associated with men and one in
which it is stereotypically associated with women. We then replace those
pronouns with placeholders, indicating the case (e.g., _< n>_ for nominative)
of each. Since WinoMT does not contain pronouns in the _possessive
independent_ case, we create these by sampling additional sentences with
_possessive dependent_ pronouns and remove the target noun. Accordingly, we
end up with 10 templates from WinoMT (2 for each of the 5 grammatical cases).
Additionally, given that WinoMT sentences are designed to be more complex and
ambiguous, we manually create two additional, simpler sentences for each
grammatical case (10 in total). In these sentences, the pronoun placeholders
refer to given names. In accordance with the WinoMT pattern, we choose the top
name stereotypically associated with women and the top name stereotypically
associated with men according to 2020 U.S. Social Security name
statistics.333https://www.ssa.gov/oact/babynames/ We show example templates in
Table 1.

Phenomon | N | A | PD | PI | R
---|---|---|---|---|---
Gendered | _he_ | _him_ | _his_ | _his_ | _himself_
_she_ | _her_ | _her_ | _hers_ | _herself_
Gender-neutral | _they_ | _them_ | _their_ | _theirs_ | _themselves_
_themself_
Neo | _xe_ | _xem_ | _xyr_ | _xyrs_ | _xemself_
_ey_ | _em_ | _eir_ | _eirs_ | _emself_
Nounself | _vam_ | _vamp_ | _vamps_ | _vamps_ | _vampself_
Emojiself |  * |  * |  *_s_ |  *_s_ |  *_self_
Numberself | _$0$_ | _$0$_ | _$0$ s_ | _$0$ s_ | _$0$ self_
Table 2: Phenomena and 3rd person pronoun sets by which they are represented
in our analysis when translating from English (en $\rightarrow$ da, de, fa,
fr, it). We list the pronouns for each grammatical case: nominative (N),
accusative (A), possesive dependent (PD), possessive independent (PI), and
reflexive (R).

We fill the placeholders with pronouns of the correct grammatical case taken
from 8 sets of pronouns that reflect diverse pronoun-related phenomena as
described by Lauscher et al. (2022). For example, we use _she/ her /her/ hers/
herself_ as an instance of gendered pronouns, and _vam/ vamp / vamps/ vamps/
vampself_ as an instance of nounself pronouns (Miltersen, 2016). The latter
are prototypically derived from a noun, and possibly match distinct aspects of
an individual’s identity. We list our test pronouns in Table 2. Our setup
allows us to test the translation of sentences containing different types of
pronouns, in all of their grammatical forms, in more and less complex
sentences and in contexts that are prone to different stereotypical
associations. Our procedure results in $164$ en sentences (4 sentences per 5
cases for each of the 8 pronoun sets plus 4 additional sentences for the
variant _themself_ instead of _themselves_).

#### Automatic Translation.

Next, we automatically translate the en source sentences to five languages:
Danish (da), Farsi (fa), French (fr), German (de), and Italian (it). We choose
these languages based on (a) typological diversity, (b) our access to native
speakers, and (c) their coverage by commercial MT. We ensure diversity with
respect to family branches, scripts, and the handling of gender and pronouns
in the languages: de and da represent the Germanic branch, fr and it the
Romanic branch, and fa the Iranian branch of Indo-European languages. da, de,
fr, and it employ the Latin script, and fa the Arabic one. Most importantly,
the handling of grammatical gender and pronouns differs among languages.
Concretely, da, de, fr, and it are gendered languages but differ in their
number of genders (e.g., de has three grammatical genders while fr has two).
While for de and it, there is currently no gender-neutral pronoun recognized
by an institutional body, for fr, the dictionary _Le Robert_ recently included
the gender-neutral pronoun _“iel”_. In contrast, fa is a gender-neutral
language. Thus, there should also be no potential for misgendering in the
resulting translations. Another interesting aspect is that two of the
languages fall under the class of _pro-drop_ languages (it, fa)444 Pro-drop
refers to a linguistic phenomenon where the subject pronoun can be omitted
from a sentence without affecting its grammaticality or clarity. It is often
clear from the verb inflection, as in Italian _“Vado”: “(I) go.”_ , while the
others do not allow for dropping the pronoun.

We focus on assessing the state of commercial MT, and accordingly rely on 3
established MT engines: Google Translate,555https://translate.google.com; we
accessed Google Translate through the interface provided in Google Sheets.
Note that we observed differences in translation when using the graphical user
interface. Microsoft Bing,666https://www.bing.com/translator and DeepL
Translator.777https://www.deepl.com/translator Currently, DeepL does not cover
Farsi (all other languages are covered by all three commercial MT engines).

#### Annotation Criteria.

While initially, we wanted to focus solely on identity aspects conveyed by the
pronouns, we noticed in an early pre-study that some of the translations
exhibited more fundamental issues. This is why we resort to the following
three categories, which allow us to answer research questions RQ1–RQ3, to
guide our analysis of a translation B based on an en sentence A: _grammatical
correctness_ , _semantic consistency_ , and _pronoun translation behavior_.

_(1) Grammatical Correctness._ We ask our annotators to assess whether
translation B is grammatically correct. Annotators are instructed to not let
their judgment be affected by the occurrence of neopronouns that are
potentially uncommon in the target language, e.g., emojiself pronouns.

_(2) Semantic Consistency._ We let our annotators judge whether B conveys the
same message as A in two variants: First, we seek to understand whether
_independent_ of how the pronoun was translated the semantics of A are
preserved. Second, we ask whether when also considering the pronoun
translation, semantics are preserved.

_(3) Pronoun Translation Behavior._ The third category specifically focuses on
assessing the translation of the pronoun. We investigate whether the pronoun
was _omitted_ (i.e., it is not present in B), _copied_ (pronoun in B is
exactly the same as in A), or _translated_ (the system output some other
string in B as correspondence to the pronoun in A). Note that none of these
cases necessarily corresponds to a translation error (or translation success)
– for instance, it might be a valid option to directly copy the pronoun from
the input in the source language to fully preserve its individual semantics.
If the pronoun was “translated”, we ask annotators to highlight its
translation, and to further indicate if the translation corresponds to a
common pronoun in the target language (and also, whether it still functions as
a pronoun). If a common pronoun is chosen, we also collect its number and its
commonly associated gender.

#### Annotation Process.

As the evaluation task requires annotators to be familiar with the target
language, the concept of neopronouns, and linguistic properties such as part-
of-speech tags, we hired five native speakers of target languages who all hold
a university degree, are proficient speakers of English, and have diverse
gender identities (man, woman, non-binary). We payed our annotators 15€ per
hour, which is substantially above the minimum wage in Italy and in line with
the main authors’ university recommendations for academic assistants.

All annotators demonstrated great interest in helping to make MT more
inclusive and were familiar with the overall topic. We took a descriptive
annotation approach Röttger et al. (2022). Each annotator then underwent
specific training in 1:1 sessions in which we showed them examples and offered
room for discussions and questions. To facilitate the task and guide our
annotators through the annotation criteria, we developed a specific annotation
interface (see Appendix). To assess the reliability of our evaluation, we
hired a second annotator for de and it to compute inter-annotator agreement
and let the same native speaker of fa re-annotate a portion of the data to
compute intra-annotator agreement (50 instances each). We measured an inter-
annotator-agreement (Krippendorff’s $\alpha$) of 0.73 for de and 0.69 for it,
and an intra-annotator agreement Abercrombie et al. (2023) of 0.86 for fa
across all upper-level categories. We thus assume our conclusions to be valid.
After completing the assessment, we gave every worker access to their
annotations with the option to change and clean their results.

###  3.2 Results

(a) Grammaticality

(b) Semantics: pronoun excluded

(c) Semantics: pronoun included

Figure 1: Overall translation quality. We show the fraction (%) of grammatically correct (a) and semantically correct (pronoun excluded (b) or included (c)) translations aggregated across all three engines for five target languages (Danish (da), Farsi (fa), French (fr), German (de), Italian (it)) given English input sentences containing the following pronoun groups: he, she (gendered); they (gender-neutral); xe, ey (“traditional” neopronouns); vam (nounself pronoun); * (emojiself pronoun); and 0 (numberself pronoun). |  Source |  MT Output | Case | Issue Type
---|---|---|---|---
1 |  _The attendant did not want to fight with the guard and gave them flowers._ |  _Die Wärterin wollte sich nicht mit dem Wachmann streiten und schenkte ihm Blumen._ | Accusative | Potential Misgendering
2 |  _Liam received a good grade, so vam was happy._ |  _Liam erhielt eine gute Note, und Vam war zufrieden._ | Nominative | Semantic Inconsistency
3 |  _Olivia found eir ring._ |  _Olivia fand einen Eir-Ring._ | Poss. Depen. | Semantic Inconsistency
4 |  _During lunch, the janitor looked for the attendant to steal eirs._ |  _Während des Mittagessens suchte der Hausmeister nach dem Besucher, um Eurren zu stehlen._ | Poss. Indep. | Pronoun Mistranslation
5 |  _Liam wanted a new haircut, so * cut the hair *self._ |  _Liam wollte einen neuen Haarschnitt, also schneiden Sie das Haar * selbst._ | Reflexive | Semantic Inconsistency
Table 3: Problems in the MT output. We show examples for en to de for
different grammatical cases, pronouns, and issue types, and highlight the
pronouns in the source sentence and the corresponding parts in the translation
in bold.

#### Overall translation quality.

We show the results on grammaticality and semantic consistency in Figures
1(a)–1(c). Depending on the target language as well as the pronoun category,
the performance varies greatly; for instance, while for gendered pronouns in
fr 95% of the translations are grammatically correct, we observe a drop of 15
percentage points for emoji-self pronouns. Even more severely, only half (!)
of the translations to it are grammatically correct when starting with the
gender-neutral pronoun _“they”_ (Figure 1(a)). We make similar observations
when asking annotators whether the meaning is preserved during the translation
process (semantic consistency): Even when not considering the translation of
the pronoun, in most cases, the performance drops when moving from a gendered
to a gender-neutral pronoun set. We note the biggest drop, 34 percentage
points, for fa and the category of noun-self pronouns (45% ) compared to
gendered pronouns with 79% (Figure 1(b)). Compared to the results for gendered
pronouns, we note the following maximum drops when aggregating over all
languages we test: 16 percentage points for grammaticality, 13 percentage
points for semantic consistency (pronoun excluded), both towards emoji-self
pronouns, and a huge drop of 47 percentage points for semantic consistency
when the pronoun is included in the assessment. We provide the aggregated
plots in the Appendix.

(a) Language

(b) Translation engine

(c) Pronoun group

Figure 2: Pronoun treatment strategies. We show the fraction (%) of
translated, copied, and omitted pronouns (a) per language (French (fr), German
(de), Italian (it), Danish (da), Farsi (fa)), (b) per translation engine, and
(c) per pronoun group in the English input sentence (_he, she_ (gendered);
_they_ (gender-neutral); _xe, ey_ (“traditional” neopronouns); _vam_ (nounself
pronoun); * (emojiself pronoun); and _0_ (numberself pronoun)).

#### Pronoun treatment strategies.

We depict the different strategies of how pronouns are treated in the
translation in Figures 2(a)–2(c). Across all languages, the engines most often
“translate” the pronouns (up to $\sim$62% for de), i.e., some non-identical
string corresponding to the en input pronoun is present in the output. The
most unpopular strategy is to omit the pronoun. Unsurprisingly, the highest
fraction of translations where this strategy is applied is present among the
pro-drop languages, fa (14%) and it (12%). Among the three translation
engines, DeepL exhibits the highest fraction of pronoun translations
(65%).888Note, however, that fa is not included due to coverage. In contrast,
GTranslate is the engine with the largest pronoun copies (43%). Interestingly,
we again observe a huge variation among the different pronoun groups: while
the gendered pronouns (_he_ , _she_) and the gender-neutral pronoun (_they_)
are most often translated (89% and 90%, respectively) and are almost never
copied to the output, our representatives of the number-self and emoji-self
pronouns most often are (74% and 68%, respectively). This is also the case for
the noun-self pronoun (_vam_) and the more traditional neopronouns (_xe_ ,
_ey_), with roughly 58% of copies each. However, for these, the fraction of
translations in turn greatly surpasses those of numberself and emojiself
pronouns, with 41% and 37%.

Figure 3: Gender conveyed by the target language pronoun (_male_ , _neutral_ ,
_female_ , _unknown (–)_) for translations that contain an existing third-
person singular pronoun. We aggregate across languages. For Italian and
French, we focus on the gender of the subject. We exclude Farsi due to its
gender neutrality.

#### Translation and Gender.

We analyze pronouns that are translated to an existing singular pronoun in the
target language in Figure 3. For the gendered source pronouns (_he, she_), the
result is roughly balanced across commonly associated genders. For _they_ , we
observe a high proportion of gender-neutral output pronouns (65%)—most often,
gender neutrality is preserved. In contrast, for different types of
neopronouns, the engines are likely to output a gendered pronoun. This finding
is most pronounced for emojiself pronouns, with 50% and 23% of output pronouns
commonly associated with male and female individuals, respectively. This
amount of translations (73%) is likely to correspond to cases of misgendering.

#### Qualitative Analysis.

For further illustration, we show examples of some problems we observe when
translating to de in Table 3. The output in Example 1 is generally correct.
However, the gender-neutral pronoun _they_ is translated to the gendered
pronoun _er_. Examples 2 and 3 show translations in which the pronoun
correspondence is copied from the input but starts with a capital letter (or
is even prepended to the succeeding word, e.g., _Eir-Ring_), as done for nouns
or names. We note a similar problem in example 4. Additionally, the output
string corresponding to the pronoun is neither copied from the input nor
corresponds to a valid word in the target language (_Eurren_). Finally, in
example 4, the emojiself pronoun appears in the output translation with the
additional 2nd person pronoun variant _Sie_.

###  3.3 Translating to English

#### Experimental Setup.

So far, we have started from en source sentences. Here, we expand our
perspective and conduct the inverse experiment: We translate _to_ en starting
from da sentences (as an example of a language with a recently emerging
gender-neutral pronoun). To this end, we start from our en templates and
manually translate these to da. We then fill the templates with the pronouns
_han_ (=_he_), _hun_ (=_she_), _hen_ (gender-neutral), resulting in 48 source
sentences. We translate those automatically with the three commercial engines
and let an English native speaker evaluate the output according to the same
guidelines.

#### Results.

The overall translation quality is relatively high; for instance, we find that
75% of translations are grammatically correct when starting from the gendered
pronouns (_han, hun_), and only see a small drop for the gender-neutral
pronoun (_hen_ with 71%). However, surprisingly, the translation engines seem
to never output the gender-neutral option _“they”_ when choosing an existing
pronoun in the target language en, not even when starting from _hen_. In
contrast, in roughly 72% of the cases, _hen_ is translated to _he_.

##  4 What Would Be a Good Translation?

Our results show that commercial engines cannot deal with pronouns as an open
word class. Often, the output is not grammatical, and the meaning is
inconsistent. Beyond these general aspects we have shown that pronoun
treatment strategies vary. Next, we seek to understand how individuals would
want their pronouns to be handled (RQ4).

Lang. | % Ment. | Pronoun sets
---|---|---
de | 35.00 | _er, sie, dey, ey, <none>_
en | 30.00 | _he, she, they, it, <no preference>_
da | 20.00 | _han, hun, den, de, she, they_
it | 7.50 | _lei, lui_
ru | 5.00 | _ он, <none>_
fa | 2.50 | _او_
Table 4: Fraction of mentions (in %) of native languages (Danish (da), English (en), German (de), Italian (it), Russian (ru), and Farsi (fa)) with associated pronouns participants of our survey identify with. Figure 4: Results for our survey question relating to MT pronoun policies. Answers in asterisks (*) were additionally provided by our participants. We show them here for completeness. Translation policy |  Translation
---|---
Referent’s name |   _Liam hat eine gute Note bekommen, also war Liam glücklich._
Ellipsis through alternative construct |   _Liam erhielt eine gute Note und war deshalb froh._
General noun (person) |   _Liam hat eine gute Note bekommen, deshalb war die Person glücklich._
Neopronoun |   _Liam hat eine gute Note bekommen, deswegen freut dey sich._
Table 5: German example translations for the English source sentence _“Liam
received a good grade, so they were happy.”_ provided by native speakers.
Participants choose various policies for preserving gender neutrality.

###  4.1 A Survey on Pronouns and MT

#### Survey Design and Distribution.

To answer this RQ, we design a survey consisting of three parts: (1) a general
part asks for the participant’s demographic information, e.g., age, (gender)
identity, as well as their pronouns in English and their native languages. (2)
The second part asks general opinions related to pronouns in artificial
intelligence. (3) The last section deals specifically with MT: here, we ask
how the individual would like their or their friends’ pronouns to be treated
when translating from their native language to another.

Participants can choose from four treatment options we identified through
informal discussions with affected individuals: _(a) Avoid pronouns in the
translation_ ; _(b) Copy the pronoun (in my native language) and don’t try to
translate it_ ; _(c) Translate to a pronoun in the target language (if
commonly associated identity matches)_ ; _(d) List multiple pronouns in the
translation possibly associated with diverse identities_. Participants can
also define additional options. We provide examples with gender-neutral
pronouns in English and encourage the participant to provide a translation in
their native language. The institutional review board of the main authors’
university approved our study design. We distributed the survey through
channels that allow us to target individuals potentially affected by the issue
and who represent a wide variety of (gender) identities. Examples include
QueerInAI,999https://sites.google.com/view/queer-in-ai and local LGBTQIA+
groups, e.g., Transgender Network Switzerland.101010https://www.tgns.ch For
validation, we ran a pre-study between March 22 and May 4, 2022 (with n=149).
The main phase was open for participation between June 18 and August 1, 2022.

#### Results.

In the main phase of our survey, 44 individuals participated. Their ages
ranged from 14 to 43, with the majority between 20 and 30. For the analysis,
we removed responses from participants under 18. The remaining participants
provided diverse and sometimes multiple gender identity terms (e.g., _non-
binary, transgender, questioning, genderfluid_) and speak diverse native
languages (e.g., English, German, Persian). The fraction of mentions of native
languages and provided pronoun sets per language are given in Table 4:
participants identify with diverse and sometimes multiple pronoun sets (e.g.,
gendered pronouns, neopronouns) as well as no pronouns. Interestingly, some
seem to use en pronouns in their non-en native language. This observation
aligns with the finding that bilinguals tend to code-switch to their L2 if it
provides better options to describe their gender identity Kaplan (2022). In a
similar vein, some participants provided only a gendered option in their
native language (e.g., _er_ in German) but indicated to identify with a
gender-neutral option in en (e.g., _they_).

Concerning the translation policies, participants chose between 1 and 3 pre-
defined options, and four provided additional ideas. The result is depicted in
Figure 4. While the most popular option is _(c) Translate to a pronoun in the
target language (if commonly associated identity matches)_ , there is no clear
consensus and also strong tendencies towards gender-agnostic solutions. This
finding is supported by the example-based analysis where we asked participants
to translate from English to their native language. Table 5 illustrates this
finding via participant answers for English to German translations (German
native speakers). Participants used different options, like using the
referent’s name or a neopronoun, to deal with the issue that there is no
established gender-neutral pronoun in German.

Additional participant comments point to the difficulty of the problem, e.g.,
_“this one’s tough because it feels like different people are potentially
going to have different desires on this one […]”_. Overall, we thus conclude
that users’ preferences are as diverse as the community itself.

###  4.2 Recommendations

Based on our observations in §3 and the survey results, we provide three
recommendations for making future MT more inclusive.

_(1) Consider pronouns an open word class when developing and testing MT
systems._ As we have demonstrated, popular commercial MT systems often fail
when gender-neutral pronoun sets are part of the input, even when translating
between resource-rich languages like en and it. Thus, NLP researchers and
practitioners must make MT more robust even with regards to fundamental
properties such as grammaticality. Extending existing data sets to reflect a
larger variety of pronouns is crucial.

_(2) If possible, provide options for personalization._ Our survey
demonstrated no clear consensus on how pronouns should be treated, and that
users’ preferences and pronouns vary. Thus, if possible, i.e., if the user is
aware of the pronouns referents in their input text identify with, and if they
directly interact with the translation engine, the decision should be left to
that user. This finding aligns with desideratum D5 for more identity-inclusive
AI identified by Lauscher et al. (2022).

_(3) Avoid potential misgendering as much as possible._ If options for
personalization are limited, no translation strategy will be ideal for all
users. However, instead of “blindly” translating, which, as we have
demonstrated, is likely to lead to misgendering, there are several other
options that translation engines could choose that exhibit less potential for
harm, e.g., gender-agnostic translations.

##  5 Conclusion

In this work, we have investigated the sensitivity of automatically
translating pronouns: small words that can convey important identity aspects.
To understand where current commercial MT stands with regards to this issue,
we started with a thorough error analysis covering six languages and three MT
engines. We demonstrated that the engines tested are more likely to produce
low-quality output when starting from gender-neutral pronouns, and we further
observed a high potential for misgendering. Emphasizing marginalized voices,
we complemented our study with a survey of affected individuals. The answers
led us to three recommendations for more inclusive MT. We hope our study will
inform and fuel more research on these issues.

## Acknowledgements

Part of this work is funded by the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation program (grant agreement
No. 949944, INTEGRATOR). Anne Lauscher’s work is funded under the Excellence
Strategy of the Federal Government and the Länder. Debora Nozza and Dirk Hovy
are members of the MilaNLP group and the Data and Marketing Insights Unit of
the Bocconi Institute for Data Science and Analysis.

## Limitations

Naturally, our work comes with a number of limitations: for instance, we
restrict ourselves to testing eight pronoun sets out of the rich plethora of
existing options. To ensure diversity, we resort to one or two sets per
pronoun group—we hope that individuals feel represented by our choices.
Similarly, we only translate single sentences and don’t investigate
translations of larger and possibly more complex texts and we only translate
to a number of languages none of which is resource-lean. Our study
demonstrates that simpler and shorter texts already exhibit fundamental
problems in their translations, even to resource-rich languages.

## Ethics Statement

In this work, we present a reality check in which we show that established
commercial MT systems struggle with the linguistic variety that is tied to the
large spectrum of identities. Consequently, this work has an inherently
ethical dimension: our intent is to point to the issue of subcultural
exclusion in language technology. We acknowledge, however, that this issue is
much bigger than the problems relating to the use of neopronouns and we hope
to investigate the topic more globally in the future.

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##  Appendix A Data Sets and Licenses

In this work, we only made use of a single existing dataset,
WinoMT111111https://github.com/gabrielStanovsky/mt_gender (Stanovsky et al.,
2019). We used the dataset to obtain en templates in different grammatical
cases, which we filled with the pronouns we test. The data set is licensed
under MIT License. We will publish our selection of sentences from WinoMT as
well as the additional sentences we added under the same license.

##  Appendix B Additional Results

We provide additional results (aggregated across languages) in Figure 5.

(a) Grammaticality

(b) Semantics: pronoun excluded

(c) Semantics: pronoun included

Figure 5: Overall translation quality. We show the fraction (%) of
grammatically correct (a) and semantically correct (pronoun excluded (b) or
included (c)) translations aggregated across all three engines and five target
languages given English input sentences containing the following pronoun
groups: he, she (gendered); they (gender-neutral); xe, ey (“traditional”
neopronouns); vam (nounself pronoun); * (emojiself pronoun); and 0 (numberself
pronoun).

##  Appendix C Annotation Interface

We show a screenshot of our annotation interface in Figure 6. The interface
was developed using HTML and JavaScript and hosted on the Amazon Mechanical
Turk Sandbox.

(a) General Questions

(b) Pronoun Treatment

(c) Translation Details

Figure 6: Screenshot of our annotation interface. The translation pair (here:
en and de) is displayed on the left side of the screen. Annotators answer the
questions shown on the right side: (a) general questions about the translation
quality, (b) questions relating to whether the pronoun was ommitted, copied,
or translated, and (c) details relating to the treatment strategy.