GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages (2024)

Abstract: Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studies on gender bias in translations into English from weakly gendered-languages, there are no benchmarks for evaluating this phenomenon or for assessing mitigation strategies. To address this gap, we introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus, that consists of human translations from Turkish, Hungarian, Finnish, and Persian into English. Each translation is accompanied by feminine, masculine, and neutral variants. The dataset, which contains between 1250 and 1850 instances for each of the four language pairs, features natural sentences with a wide range of sentence lengths and domains, challenging translation rewriters on various linguistic phenomena. Additionally, we present a translation gender rewriting solution built with GPT-4 and use GATE X-E to evaluate it. We open source1 our contributions to encourage further research on gender debiasing.

text: Findings of the Association for Computational Linguistics ACL 2024, pages 8526–8546 August 11-16, 2024 ©2024 Association for Computational Linguistics
our contributions to encourage further research on gender debiasing.

heading: 1 Introduction
text: Despite dramatic improvement in general NMT quality and breadth of supported languages over recent years (Team et al., 2022), gender bias in NMT output remains a significant problem (Piazzolla et al., 2023). One such type of gender bias is spurious gender-markings in NMT output when none were present in the source. This occurs most frequently when translating from a weakly-gendered language into a more strongly gendered one. We explore this phenomenon in translations from Turkish, Persian, Finnish, and Hungarian into English.
Gender can be marked in English through gendered pronouns (he, she, etc.) and possessive determiners (his, her), or through a limited number of
∗All authors are affiliated with Microsoft. †Contact author at ranjitan@microsoft.com.
1https://github.com/MicrosoftTranslator/GATE-XE

heading: He prepared lunch for the children. (m)
text: intrinsically gendered nouns (mother, uncle, widow, etc), many of which are kinship terms.
In each of our selected set of source languages, all personal pronouns are gender-neutral, such as O in Turkish meaning he/she/singular they. These languages do use some intrinsically gendered noun words, but not necessarily for all of the same concepts that English does. Turkish differentiates mother (anne) from father (baba), but does not differentiate nephew from niece (both are yeğen).
This difference in gender on third-person singular pronouns leads to translation scenarios such as the one seen in Figure 1, where someone with no specified gender in the source is marked as female in the translation through the pronoun she. NMT models often make gender assignments according to stereotypes (Stanovsky et al., 2019; Costa-jussà et al., 2023) - in this case a model appears to associate child care with women. One remedy for this
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category of problems is to supplement the default feminine translation with masculine and genderneutral alternatives, so that all possible gender interpretations are covered. This can be accomplished by applying a gender rewriter to the original NMT output, as shown in the bottom portion of Figure 1.
GATE (Gender-Ambiguous Translation Examples, Rarrick et al. 2023) introduced an evaluation benchmark for gender rewrites for translations from English into French, Spanish, and Italian. In this work, we introduce GATE X-E2, an extension to GATE that focuses on translations into English from a set of more weakly-gendered languages. It consists of natural sentences with strong diversity of sentence lengths and domains, and challenges translation rewriters on a wide range of linguistic phenomena. GATE X-E contains between 1250 and 1850 instances for each of our language pairs.
We also present a translation-rewriting solution that utilizes GPT-4 (OpenAI, 2022) to provide gendered and gender-neutral alternatives. It achieves high accuracy on the pronoun-only subset of GATE X-E. Finally, we also perform human evaluation and provide a detailed error analysis of the results.
The remainder of this paper is organized as follows – In section 2, we discuss the corpus creation process and structure of GATE X-E. In section 3, we discuss how various properties can affect the difficulty of translation rewriting problems. In section 4 we introduce a GPT-4-based translation-rewriting solution. In section 5 we discuss results of our experiments and perform detailed error analysis. Finally, In section 6 we cover related work.

heading: 2 GATE X-E Dataset
text: We introduce GATE X-E by describing the annotation process and labels used, as well as providing statistics on the collected data.

heading: 2.1 Arbitrarily Gender-Marked Entities
text: Following Rarrick et al. (2023), we use Arbitrarily Gender-Marked Entity (AGME) to refer to individuals whose gender is not marked in a source sentence, but is in a translation, either through a gendered pronoun or an intrinsically gendered noun. Presence of an AGME in a translation indicates that alternative gender translations are possible.
The subject pronoun from the example translation shown in Figure 1 is an AGME. Because there
2X-E indicates translation from ‘X’ language into English
is no gender marking in the source sentence, it is valid to translate the subject as she, he or they.

heading: 2.2 Dataset Creation and Annotation
text: All instances in GATE X-E consist of a single source sentence with one or more translations covering possible gender interpretations. We pulled sentence pairs for each of our language pairs from several corpora found on OPUS3: Europarl (Koehn, 2005), TED talks (Raine, 2020), tatoeba4, wikimatrix (Schwenk et al., 2021), OpenSubtitles (Lison and Tiedemann, 2016), QED (Lamm et al., 2021) and CCAligned (El-Kishky et al., 2020). We then apply the following filters:
• The source sentence scores at least 0.7 match for the intended language when using the python langdetect5 package.
• The English translation contains at least one word on a curated word list consisting of 79 English nouns (e.g. mother, uncle, actress, duke) and pronouns (he, she, him, her, his, hers, himself, and herself.) This list is found in Table 13 in the appendix.
We then sampled sentences from the filtered set and provide them to annotators. From this data, the annotators selected appropriate sentences and annotated them for entity types, number of AGMEs, and gendered-alternative translations if AGMEs are present. To be included, a translation must include at least one gender-marked term in the target, which could be a pronoun6 or noun7.
For each language, a second annotator then reviewed the data to correct errors and inconsistencies. Across pairs language pairs, the second annotator agreed with the first annotator’s assessment 95% of the time. In the remaining 5% of cases a consensus was reached after discussion between the two annotators. All of the annotators are native speakers of the source language, fluent in English, and hold advanced degrees in linguistics or a related field.
If there are one or two AGMEs in the translated pair, they will provide translation variants so that all possible gender combinations for those AGMEs (among female, male, and neutral) are covered.
3https://opus.nlpl.eu/ 4https://tatoeba.org/en/about 5https://pypi.org/project/langdetect/ 6including possessive determiners his, her, their 7including other gender-marking modifiers, such as female
They do so by replacing all gendered pronoun and noun mentions with corresponding words of the respective gender. Neutral variants are omitted if there is no suitable gender neutral term in common usage for a concept. For example, the term nibling exists as a gender neutral variant of niece or nephew, but is not in common usage and so neutral variants of translations using niece or nephew would be left out.
Some sentences may contain a mixture of references to AGMEs as well as to humans who are gender-marked in the source. In these cases, gender indicated in the source will be preserved in all translations, as in father and his will in the example shown in Figure 2. In this example, Babası explicitly indicates father in the source.

heading: 2.3 Labels
text: The labels used in GATE X-E are defined in Table 1, along with examples for each. All instances in GATE X-E refer to at least one person who is marked for gender in the English target. We include both positive and negative examples. In positive examples, at least one of those individuals was not marked for gender in the source, and is therefore an AGME, meaning that alternative translations with different gender markings are possible. In negative examples, all individuals who are marked for gender in the target are also marked in the source, so no alternative translations are possible.
Each instance will typically have multiple labels. There will always be one label indicating AGME count and each will have an associated label beginning with target_only_gendered_*, though each distinct label will be included only once. Any number of negative labels beginning with source_* may appear with any instance, as the presence of a non-AGME in a sentence does not affect AGME count.
namewill be included if an AGME is referred to by name, but there will always be another positive label as well. non-AGME-name indicates that a name appeared in the instance that was not associated with an AGME. Instances marked mixed mention at least one AGME and one individual who is gender-marked in the source.

heading: 2.4 Corpus Statistics
text: Table 4 in the appendix provides a comprehensive breakdown of corpus statistics for GATE X-E, with instance counts per language for each label.
More than half of the instances for each language pair have exactly one AGME, with around 20-30% being negative instances, having no AGMEs at all. Most AGMEs are target_only_gendered_pronoun, meaning that they have no gender markings in the source and the only words in the target that mark their gender are pronouns. This is in part because there are relatively few nouns which are gendered in English but not gendered in the source languages.
Non-AGME references involve a gendered noun in the source, and for most of the languages about half of these also include a pronoun reference.
Each language pair contains around 250 instances labeled mixed. Between 15% and 25% of instances per language have the name label, while less than 10% per language have non-AGME-name. We present the distribution of sentence lengths in source and target languages in Figure 3.

heading: 3 Translation Gender Rewriting
text: Translation gender rewriting is the process of taking a translated source-target pair and producing alternative translations with different gender markings. In a correctly rewritten translation, the gender markings should remain compatible with all gender information found in the source sentence (Habash et al., 2019). We consider this problem from the viewpoint of a user who wishes to see a set of three gendered-alternative translations with uniform output gender side-by-side: all-female, all-male and all-neutral. Because the translations will be viewed as a set, the translations should only vary from one-another in specific words that mark gender.
Here we discuss the difference in difficulty between rewrite problems where gendered nouns are included and those where gender is only marked by pronouns.
Pronoun-Only Problems
For our source languages, if the only gender markers in the target sentence are gendered pronouns, there typically cannot be gender markers in the source sentence, since those languages do not have any gendered pronouns. We can therefore assume that if we have no gender information from external context, then all individuals mentioned by a gendered pronoun in the translation must be AGMEs.
Rewriting in this scenario reduces to the relatively simple task of adjusting surface forms of all pronouns to match the desired gender. For rewrites involving he or she to gender-neutral they, some verbs must additionally be adjusted to compatible surface forms. Since our focus is on rewrites with uniform gender assignments in the output (allfemale, all-male, or all-neutral), this removes any need to determine which pronouns refer to which individual where more than one is mentioned.
Here we see an example of a Pronoun-only instance with two AGMEs:
Female + Female She gave her her umbrella. Male + Male He gave him his umbrella. Neutral + Neutral They gave them their umbrella.
Instances of GATE X-E that fall into this subtype will always have the label target_gendered_pronoun_only, and never any labels with containing gendered_noun.
Note that there is an exceptional scenario, where external context makes it reasonable for a gendered noun in one of our source languages to be translated into a gendered pronoun in English. See the example for source_gendered_noun_target_pronoun in Table 1, and refer to Appendix 10 for further discussion.
Gendered-Noun Problems
If we expand our scope to include translations containing gendered nouns, we encounter several new challenges that render the rewriting problem significantly more difficult. The following pair of examples illustates some of those new challenges.
Kardeşine ziyarete gelip gelmeyeceğini sordu.w He asked his sister if she would visit.
In this translation, both the male and female individuals are AGMEs since Kardeşine simply denotes a sibling without any gender specification. Therefore, there are nine possible rewrites, including the original translation: He and his can be optionally replaced with she/her or they/their, and sister/she can be optionally replaced with brother/he or sibling/they.
In the next example, however, the gender of the sibling is specified in the source as female by the addition of the word Kız, even though the default English translation is exactly the same:
Kız kardeşine ziyarete gelip gelmeyeceğini sordu.w

heading: He asked his sister if she would visit.
text: Here sister must remain fixed because of the gender marking in the source. She is also fixed because it is coreferent with the sister. Only the individual referred to by he and his is an AGME, so only three valid rewrites exist (including the original):
Female She asked her sister if she would visit. Male He asked his sister if she would visit. Neutral They asked their sister if she would visit.
More specifically, the additional challenges inherent in this problem class include the following.
• Gender-marked nouns may appear on the source as well, so we must examine both source and target to determine if variants are needed. This is demonstrated by the behavior of sister in the two examples above.
• Gendered pronouns in the target may refer to individuals whose gender was marked in the source, and are therefore not appropriate to modify. When multiple individuals are mentioned, we must differentiate which ones refer to such non-AGME individuals to produce a correct rewrite. This is demonstrated by the behavior of she in the two examples above.
A system that is capable of solving these problems must then be able to implicitly perform coreference resolution and alignment of nouns between the source and target, significantly increasing the complexity of a solution.

heading: 4 Experiments
text: We leverage GPT-4 to implement a translation gender-rewriting solution and evaluate it on GATE X-E.

heading: 4.1 Rewriting with GPT-4
text: Our solution uses chain-of-thought prompting (Wang et al., 2023) to elicit GPT-4 to produce three variant translations for each input sourcetranslation pair – all neutral, all feminine and all masculine, while leaving any gendered words associated with non-AGMEs unmodified. If all AGMEs had been marked with the same gender in the input translation (all masculine or all feminine), the corresponding output is expected to be identical to the input translation.
Each step in the prompt is accompanied by detailed clarifications and example vocabulary. The prompt also includes three full examples, customized per source language. The examples indicate that "None" should be returned in lieu of translation variants when there are no AGMEs present. The full prompt for Turkish-English can be found in the appendix in Figures 7 and 8.
In order to iterate and identify a suitable prompt for use in the rewriting task, we used a small dummy dataset of 100 English-Turkish sentence pairs, distinct from those in GATE X-E. During early experiments with simpler prompts, we found that GPT-4 would often make incorrect assumptions about what individuals has gender markings in the source or input translation. We found that making instructions much more explicit helped reduce the frequency of these assumptions.

heading: 4.2 Data Preparation
text: Each GATE X-E instance consists of a source sentence and a set of translations in English. Each positive instance is used multiple times during evaluation. More formally, we transform each instance into multiple test tuples, each corresponding to a different evaluation setting.
Each test tuple consists of a source sentence, an original translation and a rewriter reference output. In all cases we use one of the instance’s reference translations as the test tuple’s original translation, and another reference translation from the same instance as the rewriter reference output. All-feminine and all-masculine original translations are paired with opposite-gender and allneutral rewriter reference outputs. In instances with
two AGMEs, we also evaluate each non-neutral mixed-gender reference as an original translation (i.e. feminine/masculine and masculine/feminine) and pair with all-feminine, all-masculine and allneutral rewriter reference outputs.
Negative instances, which do not include AGMEs and should not be modified by the rewriter, are handled separately. For each negative instance we create tuples to check that the rewriter’s feminine, masculine and neutral outputs are all unmodified from the original translation.
At the top level, we group the above-described test tuples by the gender-assignments of AGMEs in their original translation, producing four subsets: feminine, masculine, mixed-feminine-andmasculine (with 2 AGME), and negative. These correspond to major sub-categories in our evaluation results.
For simplicity, we exclude the handful of instances with three or more AGMEs. We do not include any tuples that include gender-neutral forms in the original target because of the ambiguity in distinguishing singular neutral pronouns from plural they forms. For any instance where the neutral reference is empty (e.g. because there is no neutral form of a term), no test tuple with neutral rewriter reference output is created.
Each test tuple is also marked with a subtype. If it came from an instance containing any intrinsically gendered nouns in either source or target, it is designated Gendered-Noun. Positive tuples from instances without gendered nouns must have target-side gendered pronouns as their only gender markings and are designated Pronoun-Only. Note that negative examples always contain a gendered noun in the source, and so they do not have a distinction between the two types.
Table 5 in the appendix shows counts for each source language and category. Note that each tuple with an all-feminine original translation has a corresponding one that is all masculine, so the counts are identical for these sets, marked (f/m) in the table.

heading: 5 Results
text: Table 2 shows the accuracy of our solution on test tuples over each combination of source language, and subtype, with Overall indicating an aggregate score over Pronoun-Only and Gendered-Noun. The top labels in the header row indicate gender of AGMEs in the original target, and the bottom indi-
cates the desired output gender. Following Rarrick et al. (2023), we focus on exact match accuracy to the reference. Frequently only one or two words will be different between an original translation and a correct rewrite. In this context, metrics such as BLEU (Papineni et al., 2002) and WER are not very effective at determining the significance of single extraneous or missed word modifications. Therefore, credit is only given for a test tuple when the rewriter output exactly matches the reference output.

heading: 5.1 Pronoun-Only Subset
text: On the Pronoun-Only subset, the solution rarely makes mistakes for masculine and feminine original targets, with scores ranging from 0.96 to 0.99. Test cases where the original target has mixed gender all come from 2-AGME instances. These skew towards longer and more complicated sentence, which thus leads to slightly lower accuracy.
On most language pairs we see that PronounOnly rewrites into masculine outperform rewrites into feminine by a few percentage points. The largest gap is 5 points for mixed-gender original target on Hungarian. This may indicate a slight general tendency of GPT-4 to prefer phrasing using masculine pronouns.

heading: 5.2 Gendered-Noun Subset
text: Scores on the Gendered-Noun subset are substantially lower than for Pronoun-Only, generally ranging from about 0.5 to 0.8, with Finnish mixed→feminine as an outlier at the low end at 0.34. The score differential with Pronoun-Only can be mostly attributed to the more difficult nature of
the problems. However, there are some cases where there are multiple acceptable alternative phrases to use in a rewrite, and GPT-4 chooses a different one from the reference.
This effect is most pronounced in the Finnish Complex mixed-original target data. This subset contains a large amount of data from Europarl that includes titles such as Mr. and Mrs. and addresses to Mr. President. The feminine rewrites often choose a mismatched form, such as Ms. Müller rather than Mrs. Müller, or Mrs. President rather than Madam President.
Similarly, neutral rewrites on such sentences often produces Honorable President, Chairperson, or Honorable Speaker as a rewrite of Mr. President or Madam President, mismatching Honored President in the reference.

heading: 5.3 Negative Subset
text: Negative→Gender score indicates how often both the feminine and masculine outputs produced by our solution exactly matched the original translation, while Negative→Neutral measures the same for the neutral output. An output of None from GPT-4 is also possible, indicating that all variants should be considered a copy of the original translation. On the negative data subset, this is considered a reference match.
For Turkish, Finnish and Hungarian, we see scores for both Gender and Neutral subsets in the 0.8 to 0.9 range. Farsi is an outlier with 0.54 and 0.53 at the low end.
With the exception of Turkish, we find that we almost never see matches on negative test items aside from None outputs. For Turkish, 47% of
non-None outputs match for gendered and 15% of neutral outputs. For all languages, however, we do see a large number of neutral outputs that match a version of the original translation where all pronouns are modified to their neutral variants8. This occurs in particular when there are gendered nouns in the source which determine the gender of some pronouns in the translation as well. For example, instead of the man has something under his coat, it would output the man has something under their coat.
If were relax matching criteria to allow this variant, neutral negative accuracy increases to 0.91 for Farsi, 0.92 for Turkish, 0.95 for Finnish and 0.95 for Hungarian.

heading: 5.4 Gender-Neutral Rewriting
text: For most settings, we observe that accuracy is similar whether rewriting into gendered or genderneutral output. The most obvious outlier is that for Gendered-Noun mixed-gender original translations, neutral output scores are consistently significantly lower than the masculine output scores. Some of this gap can be attributed to mismatches in noun forms, such as Honorable Speaker and Honored Speaker as mentioned above.
We also see a few other data points where neutral outputs significantly trail their gendered counterparts on Gendered-Noun sets: Farsi feminine and masculine original targets, and Finnish feminine original target.

heading: 5.5 Human Evaluation
text: For the Turkish data, one of the Turkish annotators provided annotations for error type on all outputs that did not exactly match the reference. These are found in Table 3.
8and verb agreement modified to match
For each test item, they mark whether there nouns or pronouns were changed where the reference and original translation matched (extraneous noun/pronoun change, as well as whether any nouns or pronouns should have been changed but were not (missing noun/pronoun change). Distributions for masculine and feminine outputs were similar, so we show combined gendered outputs for each subtype. We also aggregate over mixedand uniform-gender inputs.
For positive test cases, missing noun and pronoun changes were far more common than extraneous changes. For cases containing gendered nouns, noun changes were missed more often than pronoun changes, while extraneous pronoun changes were more common than extraneous noun changes.
For Negative test cases, gendered output only ever contained extraneous pronouns changes, while neutral outputs did have a fair number of extraneous noun changes. An example of these is changing man to person even when man was gender-marked in the source sentence.
Among missing pronoun errors, missing possessive determiners was by far the most common, with subject and object pronoun errors roughly equivalent. Missed and extraneous reflexives were extremely rare. We also saw a single case of subjectverb agreement error each when changing he and she to they, and one case where other wording was incorrectly changed in the sentence.

heading: 6 Related Work
text: Understanding and Assessing Gender Bias: It has been documented that MT systems often make mistakes and show gender biases when translating between languages with differing gender norms (Stanovsky et al., 2019; Prates et al., 2019; Rescigno et al., 2020; Lopez-Medel, 2021; Prates
et al., 2019; Fitria, 2021; Saunders and Byrne, 2020). Costa-jussà et al. (2023) show an 8-bleupoint gap between masculine and feminine references when translating from non-gender-marked English sentences into various strongly-gendered langauges.
Měchura (2022) proposes a taxonomy for describing situations where properties such as gender, number or formality are unknown in a source sentence but assumed in a translation, resulting in bias. Evaluation Benchmarks: Translating from English: Bentivogli et al. (2020) and Savoldi et al. (2022) introduced the MuST-SHE dataset, which includes triplets of audio, transcript, and reference translations for English to Spanish, French, and Italian languages, classified by gender. Stanovsky et al. (2019) developed the WinoMT challenge set, which includes English sentences with two animate nouns, one of which is coreferent with a gendered pronoun.
Renduchintala et al. (2021) introduced the SimpleGEN dataset for English-Spanish and EnglishGerman language pairs, which includes short sentences with occupation nouns and clear gender indications. The Translated Wikipedia Biographies dataset includes human translations of Wikipedia biographies for gender disambiguation evaluation. Lastly, Currey et al. (2022) presented the MTGenEval dataset, which includes gender-balanced, counterfactual data in eight language pairs, specifically focusing on translation from English into eight widely-spoken languages.
Translating into English: Numerous studies have focused on evaluating bias in translating from from a weakly gendered language such as Turkish into English. (Prates et al., 2019; Fitria, 2021; Ciora et al., 2021; Ghosh and Caliskan, 2023). Strategies for Gender Bias Mitigation : Habash et al. (2019) propose a gender-awareness wrapper for Arabic MT systems and develop a corpus for first-person-singular gender identification and reinflection. Alhafni et al. (2020) present an Arabic sentence-level gender reinflection approach using linguistically enhanced sequence-to-sequence models. Alhafni et al. (2022) define gender rewriting in Arabic contexts involving two users and develop a multi-step system combining rule-based and neural rewriting models.
To mitigate gender bias when translating queries that are gender-neutral in the source language, Google Translate announced a feature (Kucz-
marski, 2018; Johnson, 2020) that provides genderspecific translations. Both Sun et al. (2021) and Vanmassenhove et al. (2021) have explored monolingual gender-neutral rewriting of English demonstrating that a neural model can perform this task with reasonable accuracy. Ghosh and Caliskan (2023) evaluate gender bias in GPT-3.5 Turbo output when translating from gender-neutral languages into English. To the best of our knowledge, our work is the first to leverage GPT-4 for mitigation.
Saunders and Byrne (2020) manually construct a gender-balanced profession dataset and use it to fine tune MT models to improve gender accuracy when source gender is unambiguous. Piergentili et al. (2023) provide an in depth discussion of the challenges and intricacies of generating gender-neutral translation output, focusing on English-Italian translation.

heading: 7 Conclusion
text: We have presented GATE X-E, a diverse dataset covering a wide range of scenarios relevant to translation gender-rewriting for English-target language pairs, covering gendered and gender-neutral rewrites. We have discussed intricacies of Englishtarget translation rewriting, and explained what properties lead to easier or more difficult rewrite problems. We have explored the ability of GPT-4 to provide rewrites for these translations, showing that it can achieve very high accuracy on pronounonly rewriting problems, but performs less well when gendered nouns are introduced.
In the future we hope to expand the set of source languages beyond the four weakly gendered languages used in this work, to include those with different gender nuances. As can be seen from GPT4’s inconsistent performance in translation genderrewriting, particularly when gendered nouns are introduced, additional methods need to be explored in order to achieve sufficiently accurate and unbiased translation alternative sets.
We also hope that by making GATE X-E accessible to the broader research community, we can encourage further research on gender debiasing in the machine translation space.

heading: 8 Limitations
text: Our study has some limitations that could be addressed in future research. Firstly, while we utilized GPT-4 for rewriting tasks, the potential of opensource models remains unexplored and could be
beneficial. Secondly, our rewriter operates on fewshot chain-of-thought prompting. Future investigations could consider exploring the zero-shot setting, which could potentially be more cost-efficient.
During construction of our datasets, all four language pairs were given equal priority, and they are of roughly equal quality to the best of our knowledge. However, during error analysis, we were only able to secure sufficient budget to perform human annotation for a single language pair, EnglishTurkish.

heading: 9 Ethical Considerations
text: We acknowledge that our work is currently limited to English as the target language and four weakly gendered languages - Turkish, Hungarian, Finnish, and Persian - as the source languages. This focus may inadvertently create a bias towards these specific languages and their unique gender structures, potentially limiting the applicability of our findings to other languages with different gender nuances.
Additionally, our challenge set was constructed with the assistance of bilingual linguists for each language pair, which may introduce another layer of bias based on their individual interpretations and understanding of gender in language. While we plan to expand the scope of source languages in future work, these current limitations should be considered when interpreting our results.
Our work also explores generation of genderneutral translation variants in English. The genderneutral variants are limited to those following a singular they pattern and do not cover the full range of possible gender-neutral or non-binary pronouns.
Linguists who we recruited to carry out annotation tasks were sourced and managed through a linguistic services agency. They were paid on an hourly basis.

heading: 10 Acknowledgements
text: We thank Huda Khayrallah, Roman Grundkiewicz and Vikas Raunak for their valuable feedback. The study would not have been possible without the contribution of our annotators.

heading: A Further details on GATE X-E
text: Table 4 provides a comprehensive breakdown of corpus statistics for GATE X-E, with instance counts per language for each label. Please note that each instance will typically contain multiple labels. For example, a 1-AGME instance will have one positive label and zero or more negative labels, as any number of other non-AGME individuals may be referenced.
Figure 3 presents boxplots that demonstrate the sentence length distribution on source and target for four language pairs: Finnish to English, Hungarian to English, Persian to English, and Turkish to English. The left plot shows the sentence lengths in the source languages, and the right plot displays the sentence lengths in English, the target language. The legend indicates the color corresponding to each language pair. Compared to the other three language pairs, Finnish to English contains longer sentences.
Table 5 shows counts for each source language and category. Note that tuples with uniform gender original translations (f/m) are always created in pairs, so they have the same counts.

heading: B Monolingual Rewriting with GPT-3.5 Turbo
text: GPT-4 performs very well on the pronoun-only subset of examples. However, its inference cost is high. Therefore, we evaluate the pronoun-only subset using GPT-3.5 Turbo.
B.1 Gender-Neutral Rewriting As shown in 3, pronoun-only uniform-gender do not require access to source information, and so our GPT-3.5 Turbo solution is only given access to original translation target. We first use GPT-3.5 Turbo to produce an all-neutral rewrite and then use a rule based solution to convert the all-neutral rewrite to gendered rewrites.
The trickiest aspects of the gender-neutral rewrite are disambiguating pronoun classes for her and him, and adjusting verb forms when subjects change from she/he to they, so these are the primary decisions that GPT-3.5 Turbo must make.
We experiment with zero-shot and few-shot approaches. The zero-shot approach uses a singlesentence prompt as seen in Figure 5. The few-shot approach expands on this prompt by adding five examples, and it can be seen in Figure 6.
B.2 Gender-Neutral to Gendered Rewriting A useful simplifying observation is for uniformgender pronoun only rewrites, we can generate a correct feminine or masculine rewrite from the original target and a correct all-neutral rewrite. Referring back to 12, we see that all elements in the neutral column are unique, while the masculine and feminine columns each have one surface form fitting two categories. Knowing the correct neutral pronoun fully determines what pronouns should be used for a given gender.
In practice the neutral rewrite may contain errors. To minimize their impact on the gendered rewrites, we begin with the original translation, and map to pronouns directly to the desired gender where unambiguous. When ambiguous (i.e. for her or his in the original target), we rely on the chosen form of the neutral pronoun to disambiguate.
B.3 Experiments B.3.1 Rewriters Neutral rewriter Systems: We consider the following rewriting systems:
1. Rule-based system proposed by Sun et al. (2021): It uses Spacy and GPT-2 to resolve
ambiguity with his and her, and to adjust verb forms as needed. They also trained a neural model, but it was unfortunately not accessible. 2. GPT-3.5 Turbo: We evaluate GPT-3.5 Turbo on zero-shot and few-shot settings, using the prompts shown in Figures 5 and 6 in the appendix.
We investigated the neural model introduced by Vanmassenhove et al. (2021) as well, but were unable to reproduce results on their test data.
For all GPT-based rewrites we set temperature T = 0
B.3.2 Evaluation We report the rewriter systems’ performance using BLEU (Papineni et al., 2002), Word Error Rate (WER), and Accuracy.
In the gender-neutral rewriting task (Table 6), GPT-3.5 Turbo performs better in the few-shot setting compared to the zero-shot setting. Although
GPT-3.5 Turbo provides slightly higher accuracy compared to the rule-based system proposed by Sun et al. (2021), the rule-based system performs better based on BLEU and WER. This is because GPT-3.5 Turbo makes modifications unrelated to neutral rewriting, as detailed in the error analysis section.
In the gendered-alternatives rewriting task (Table 6), the zero-shot setting indicates that for resolving the her→his/him and his→her/hers ambiguity, gender-neutral rewrites from the zero-shot prompt are used. Similarly, the few-shot setting uses the corresponding gender-neutral outputs from the fewshot prompt. The performance of both settings is comparable.
B.3.3 Error Analysis
Figure 4 illustrates the distribution of aggregated errors across four language pairs for GPT-3.5 Turbo in both zero-shot and few-shot settings, specifically for the task of gender-neutral rewriting. The definitions of these errors are provided in Table 8 in the appendix, while Table 9 offers examples for each error label.
In both settings, the majority of errors stem from modifications unrelated to gender-neutral rewriting and from instances where the model suggests no changes are necessary to render the input text gender-neutral. Additional examples of errors due to unrelated modifications can be found in Table 10 in the appendix. The few-shot setting, however, does show an improvement in neutral rewriting errors (such as POS(part-of-speech) errors and them being rewritten as themselves) when compared to the zero-shot setting.
Tables 11 presents the error distribution for each of the four languages. Upon closer examination of the Finnish data, which has the highest error rate, we found that the errors are primarily due to the longer input length. This increases the scope for modifications of the text that are unrelated to gender-neutral rewriting.

heading: C Prompting Templates
text: C.1 GPT-3.5 Turbo Prompts
Figures 5 and 6 show the GPT-3.5 Turbo zero-shot and few-shot prompts used in the gender-neutral rewriting task. We use the same prompt across all the language pairs as the task is source agnostic.
C.2 GPT-4 Prompts
The full prompt for Turkish-English can be found in Figures 7 and 8. Prompts for other languages use the same structure, but examples are customized to fit those languages.

heading: D Rewriting with GPT-4
text: Our solution uses chain-of-thought prompting (Wang et al., 2023) to elicit GPT-4 to produce three variant translations for each input sourcetranslation pair – all-neutral, all-female and allmale, while leaving any gendered words associated with non-AGMEs unmodified. We ask it to work step-by-step through the process of identifying AGMEs before finally rewriting the original translation:
• Identify unique individuals mentioned in the target, as well as any gendered words that refer to them.
• Identify words in the source that refer to those same individuals.
• Determine which source words mark for gender.
• Designate any individuals referred to by gendered words in the target, but not in the source as AGMEs
• Produce a neutral, feminine and masculine variant translation where any gendered words referring to AGMEs are modified to match the respective gender.
Each step in the prompt is accompanied by detailed clarifications and example vocabulary. The prompt also includes three full examples, customized per source language. The examples indicate that "None" should be returned in lieu of translation variants when there are no AGMEs present. The full prompt for Turkish-English is shown found in Figures 7 and 8.

heading: E Mitigation Strategies Based on Source
text: Rather than rewriting English-target translations into feminine, masculine, and neutral forms, one could use the source sentence as input to create these three variants directly. This section explains how GATE X-E can be employed to assess such a system.
The first step is to verify that the generated feminine, masculine, and neutral variants are the same, except for changes related to gender. This is a crucial step as it ensures that the meaning of the translation remains consistent, regardless of the gender. If there are differences in the translations beyond the gender-related changes, it could imply that the translation is not accurate or is introducing additional bias. After this, the generated output can be compared with the feminine, masculine, and neutral references provided in GATE X-E using contextual MT evaluation metrics.
Kuczmarski (2018) initially explored a sourcebased debiasing approach in which they enhanced a Neural Machine Translation (NMT) system to produce gender-specific translations. This was achieved by adding an additional input token at the beginning of the sentence to specify the required gender for translation (e.g., <2FEMALE> O bir doktor → She is a doctor). However, they encountered challenges in generating masculine and feminine translations that were exactly equivalent, with the exception of gender-related changes. As a result, they later switched to a target-based rewriting approach in their subsequent work (Johnson, 2020).

heading: Future Work
text: While we plan to expand the scope of source languages in future work, these current limitations should be considered when interpreting our results. Our work also explores generation of genderneutral translation variants in English. The genderneutral variants are limited to those following a singular they pattern and do not cover the full range of possible gender-neutral or non-binary pronouns. Linguists who we recruited to carry out annotation tasks were sourced and managed through a linguistic services agency. They were paid on an hourly basis