“I’m” Lost in Translation: Pronoun Missteps in Crowdsourced Data
Sets
Katie Seaborn

Tokyo Institute of Technology
seaborn.k.aa@m.titech.ac.jp

ABSTRACTAs virtual assistants continue to be taken up globally, there is anever-greater need for these speech-based systems to communicate
naturally in a variety of languages. Crowdsourcing initiatives havefocused on multilingual translation of big, open data sets for use
in natural language processing (NLP). Yet, language translation isoften not one-to-one, and biases can trickle in. In this late-breaking
work, we focus on the case of pronouns translated between English
and Japanese in the crowdsourced Tatoeba database. We foundthat masculine pronoun biases were present overall, even thoughplurality in language was accounted for in other ways. Importantly,
we detected biases in the translation process that reflect nuanced
reactions to the presence of feminine, neutral, and/or non-binary
pronouns. We raise the issue of translation bias for pronouns andoffer a practical solution to embed plurality in NLP data sets.

CCS CONCEPTS• Human-centered computing; • Human computer interaction (HCI);; • Social and professional topics; • User Characteristics; • Gender;; • Computing methodologies; • Artificial
Intelligence; • Natural Language Processing; • Lexical semantics;

KEYWORDS
Gender bias, Data sets, Natural language processing, Translation,
Feminist HCIACM Reference Format:
Katie Seaborn and Yeongdae Kim. 2023. “I’m” Lost in Translation: Pronoun
Missteps in Crowdsourced Data Sets. In Extended Abstracts of the 2023 CHI
Conference on Human Factors in Computing Systems (CHI EA ’23), April
23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3544549.3585667

1

INTRODUCTION

Initiatives in natural language processing (NLP) for communication
between people and virtual assistants (VAs) is taking off [9, 28, 36].
Many are gathering and offering large, crowdsourced data sets forthis purpose [11]. An ongoing challenge is that most are in English
and geared towards the US context [12, 20, 23]. In recognition ofthis, new efforts have started to be undertaken to ensure that adiversity of languages and cultural contexts are represented. For
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CHI EA ’23, April 23–28, 2023, Hamburg, Germany© 2023 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-9422-2/23/04.
https://doi.org/10.1145/3544549.3585667

Yeongdae Kim

Tokyo Institute of Technology
kim.y.ah@m.titech.ac.jp
example, the Amazon Alexa team have been spearheading a “massive” crowdsourced translation and localization initiative of their
MASSIVE data set into 51 languages [19], including a global competition1 . Indeed, most of these initiatives rely on crowdsourcing
activities. However, critical analyses have identified biases in translation, notably around gender representation in pronouns [32, 41]
and word choice [10, 32, 38]. As such, alongside the move towards
voice- and speech-based interaction [15, 34], gender bias in NLP is
now a hot topic for feminist HCI work [3, 4, 6, 13, 32].
One issue that remains underexplored is how pronouns are translated language-to-language [14, 21, 30]. Languages vary greatly on
pronoun use. For example, in English, a small set of pronouns arecommonly used, but in Japanese, pronouns are less commonly used
and arguably more varied [29]. We also need to consider genderambiguous or “gendered ambiguous” pronouns that could represent
more than one (or no) gender identity [41]. A common example is"they/them," which can be interpreted as a neutral plural pronoun
or a singular non-binary pronoun. Moreover, trans*, non-binary,and other genderqueer folk may use “gender neutral” pronouns
like “they/them” or gendered pronouns like “he/him” and “she/him,”
as well as a diversity of other pronouns, such as “ze/xe/xem/zem”[22, 25, 33, 35]. Translators need to make careful choices about how
to translate pronouns in a case-by-case fashion, and also be awareof the cultural context of variable pronouns. However, crowdsourcing contributors are likely not trained in such matters, as evidenced
by the small body of work on pronoun translation for NLP data
sets so far [32] and when considering related areas, such as crowdsourcing efforts and user-generated content for machine translation[14, 21, 30, 42]. To the best of our knowledge, no work to date has
looked at the context of NLP data sets geared towards VA training.
As a first step, we evaluated pronoun translations in Tatoeba2 , anopen NLP database featuring crowdsourced translations between
English and Japanese. We chose the case study of pronouns as
an important identity, diversity, and inclusivity marker [25, 31,33], as well as a pluralistic facet of language with implications fortranslation. Our overall research question (RQ) was: Are there genderbiases in pronouns within crowdsourced translations of English data
sets to Japanese? Our aim was to evaluate translation accuracy, so
we first asked: RQ1: Have pronouns been translated faithfully fromEnglish to Japanese? We then focused on two important aspects
when translating pronouns: (i) the relative number of pronouns
linked to certain genders and (ii) whether one-to-one translationsof pronouns were faithfully preserved. We were guided by longstanding language biases in English and romantic languages [5],recently confirmed within the latest crowdsourced English NLP data
sets [8, 17, 18, 32] and may thus feature in translations of these data
1 The Massively Multilingual NLU 2022 workshop (MMNLU-22 at EMNLP 2022: https://mmnlu-22.github.io
2 https://tatoeba.org

CHI EA ’23, April 23–28, 2023, Hamburg, Germany

sets to Japanese and other languages. As such, we asked specificquestions about pronoun translation: RQ2: Is there a masculine bias
in pronouns across the data sets? And RQ3: Are there more instancesof mistranslated masculine pronouns than other pronouns? We aim
to raise awareness of sociocultural biases that may be embeddedin translation work and offer a technical solution to account for
pronoun diversity and context-sensitive variability. We contributeour findings on the most recent and well-known NLP data sets used
for training VA speech and a way to embed plurality in pronouns.

2

RELATED WORK

We situate our work at the intersection of translations of NLP data
sets and social justice initiatives within HCI.

2.1

Gender Mistranslations in NLP

While virtually no work has considered gender translation biases
in NLP data sets, a growing body of work has considered machinetranslation [14, 21, 30]. Google Translate, for instance, is a longstanding example of a gender biased system that relies on usergenerated content; it specifically mistranslates pronouns and assigns masculine pronouns in the presence of ambiguous pronouns
or a lack of pronouns [30, 42]. Moreover, recent examples of massscale translation efforts in NLP spaces, specifically for VAs, indicatesthat biases, including gender biases, have not been considered. For
example, the authors of the MASSIVE data set do not mention bias
in their report3 . As such, we hypothesize:
H1. There will be mismatches in direct pronoun translations from English to Japanese.

2.2

Masculine Biases in NLP

A wealth of research on NLP and machine translation has pointed
to gender biases and especially masculine biases [10, 12, 20, 32].
A recent word embeddings study of over 630 billion sources on
the Internet evaluating the word “person” and “people” [2] foundthat these “gender neutral” words are often equated with “man”
and “men.” Breaking work by Seaborn, Chandra, and Fabre [32] hasrevealed implicit masculine biases in MASSIVE as well as the older
ReDial NLP data sets, designed for training VAs and conversationaluser interfaces. This is not unprecedented, nor localized to NLP;
this is a human language and cultural phenomenon. For instance,
when there are no gender markers, such as names or titles, or
pronouns, people are given to assume the person is a man [1]. We
thus hypothesize for Tatoeba:
H2. There will be a masculine bias in pronoun use in
the data sets.
Moreover, given the literature on crowdsourcing and usergenerated content for machine translation alongside likely genderbiases, we would expect contributors who make translations tocarry over biases from one language to another. Indeed, a growing
number of critical voices within NLP spaces have raised awarenessof gender and other biases that can creep into the creation process,from who created the data to who curated and annotated it [7, 27].
We expect this to be true for translation, which outside of NLP is
recognized to be a process where biases come into play [14, 39],
3 https://arxiv.org/abs/2204.08582

Katie Seaborn and Yeongdae Kim

including as a creative part of localizing and situating work within
other cultures, i.e., transcreation [16]. As such, we hypothesize:
H3. There will be a masculine bias in pronoun translation from English to Japanese.

3

METHODS

We conducted an automated content analysis of pronoun use in
English and Japanese within Tatoeba. For this, we used NLP methods to extract meaningful patterns about pronoun presence anduse, especially in translation, from the text materials in the data
sets. Our protocol was registered before data collection on January
15th , 2023 via OSF4 .

3.1

Data Set

We used the Tatoeba database of crowdsourced sentences and theirtranslations across 359 languages, specifically the English and Japanese data sets. The downloadable version of the database is updated
every Saturday at 6:30 a.m. (UTC); we used the version from December 24th , 2022. While it is difficult to link translations to certain
people, statistics indicate that contributors total 7,027 native5 and13,558 users6 of English and 431 native and 2,997 users of Japanese.
The data sets are made up of numbered rows, such that each row ispaired with a row in the other language data set. Here is an example
from the Japanese and English data sets:
Line 51: が最後に自分の考えをえた人は、をいだと思ったようだ。
Line 51: The last person I told my idea to thought I
was nuts.
Tatoeba was originally created by Trang Ho in 2006 and hosted
on SourceForge as “multilangdict.” Coincidentally, “tatoeba” is aJapanese word that means “for example.” We chose Tatoeba for a
few reasons. It is a top-ranked and downloaded resource at Hugging
Face, a provider of NLP resources7 . A diversity of sentences arecontinually being added to the database, which can be used to train
and update the training of VAs and other NLP systems. As an open
data set, it has a CC BY 2.0 FR licence, though some sentences
are public domain. Notably, a portion of the English and Japanese
sentences were sourced from the public domain Tanaka Corpus [40].
As a big data set, it has 255,675 sentences at the time of writing. It isan established and active crowdsourcing project, with participantsaround the world.

3.2

Data Preparation and Analysis

All data analyses were performed in Python (version 3.8.10) with the
Tatoebatools (version 0.2.1) package8 . This package allowed us todownload the Tatoeba data sets in Python. The English data set was
tokenized, i.e., sentences were divided into separate words/terms,
and tagged according to their linguistic meaning, e.g., nouns, pronouns, verbs, etc., using the Natural Language Toolkit (nltk) package (version 3.8.1). The Japanese data set was tokenized and tagged
4 https://osf.io/8jmyc

5 https://tatoeba.org/en/stats/native_speakers

6 https://tatoeba.org/en/stats/users_languages

7 https://huggingface.co/datasets/tatoeba
8 https://pypi.org/project/tatoebatools

“I’m” Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets

CHI EA ’23, April 23–28, 2023, Hamburg, Germany

using the Janome9 (version 0.4.2) package. For data preprocessing,
we filtered out Japanese special characters, such as full-width spaces,‘, 、,’, “,”, etc. Then we extracted pronouns from the English sentences using the pronoun tags created by nltk, namely the PRP and
PRP$ tags, and the 代名(dai-mei-shi) tag created by Janome fromthe Japanese sentences (refer to 3.3 for details). We identified thegenderedness of the Japanese pronouns by referring to Jisho.org10 ,which is an online Japanese dictionary that uses Tatoeba to provide
example sentences between Japanese and English. We developed
three pronoun categories: masculine, feminine, and, to account for
pronoun ambiguity [41], ambiguous, including singular first-person
pronouns, singular and/or plural “they/them” pronouns, words that
may or may not be pronouns, such as 何(nan; can refer to a personor “what”), and gender-diverse pronouns used by genderqueer people, such as “xim/xir,” that could be pronouns or represent truncatedwords or typos [32], which only manual analysis could confirm.
We generated descriptive statistics, i.e., means (M) and standard
deviation (SD) of counts and ratios/percentages. We created a confusion matrix (or error matrix), which maps the relative distributionof one or more variables before and after algorithmic treatment [37]and is often used for automated content analysis and classificationin machine learning (ML). We used inferential statistics, i.e., Chisquare tests, to compare groups, i.e., pronoun genders, languages,
matching or not matching translations.

Table 1: Descriptive statistics by pronoun category and language.

3.3

We now turn to answering the hypotheses following data analysis.

Pronoun Tags

We used the pronoun dictionary provided by the nltk package’s Partof Speech (POS)11 library for the English data set, and the tokenizer
created by Kudo, Yamamoto, and Matsumoto [24] for the Japanesedata set. However, because these libraries did not represent an upto-date or inclusive spread of pronouns, we manually added several
pronouns according to recent NLP work [25]. The lists of pronouns
that we used are as follows:
Japanese masculine:
きみ、君、お前、俺、彼、彼ら、、君たち、オレ、おまえ、ぼく、ボク、ら、、おれ、吾、キミ、てめぇ、小生、てめえ、たちkimi, kun, omae, ore, kare, karera, boku, kuntachi, ore,omae, boku, boku, bokura, bokutachi, ore, wagahai,
kimi, temē, shōsei, temē, bokutachi
Japanese feminine:
彼女、あたし、彼女ら
kanojo, atashi, kanojora
Japanese ambiguous:
何、私、それ、あなた、みんな、あいつ、 、
わたし、方、どなた、我々、あんた、そっ、やつ、みな、奴、あれ、なに、皆、みなさん、みなさま、我ら、余、彼等、だれ、奴ら、ウチ、
わたくし、われわれ、よそ、われ、奴等、己、おのれ、何れ、わし、彼奴
nani, watashi, sore, anata, minna, aitsu, dare, watashi,
anata, donata, wareware, anta, so, yatsu, mina, yakko,
9 https://mocobeta.github.io/janome

10 https://jisho.org

11 https://www.nltk.org/book/ch05.html

Gender Category

EnglishJapanese

Masculine
Feminine
Ambiguous
Non-Masculine

43,45319,025
148,903167,928

50,35017,123
61,91679,039are, nani, mina, minasan, minasama, warera, yo, karera, dare, yatsura, uchi, watakushi, wareware, yoso,
ware, yatsura, onore, onore, izure, washi, aitsu
English masculine: he, him, his, himself
English feminine: her, she, herself
English ambiguous:I, you, it, me, my, your, them, their, myself, they, themselves, we, us, oneself, our, yourself, its, itself, self, ourselves, ’em, theirs, thyself, one, ours, themself, theirself, theirselves, theirselves, xe, xem, xim, hir, xemself,
xemselves, hirself, hirselves, ze, zeself, zeselves

4
4.1

RESULTS
H1. There will be mismatches in direct
pronoun translations from English to
Japanese.

A Chi-square test was run to see if the number of pronouns by pronoun category (Table 1) differed between the English and Japanesedata sets, i.e., because of the translation process, where pronouns
were classified into three categories: masculine (EN = 43,453, JP =50,350), feminine (EN = 19,025, JP = 17,123), and ambiguous (EN =148,903, JP = 61,916). A medium, statistically significant relationship was found, 𝜒2(2, N = 340,770) = 17,802.0, p < .001, Cramer’s
V = .23. Overall, this confirms that a significant change occurred
through the translation process, so we can accept H1.

4.2

H2: There will be a masculine bias inpronoun use in the data sets.

Chi-square tests with Yates corrections were run to compare masculine and non-masculine pronouns by language. A statisticallysignificant difference comparing masculine and feminine pronouns
to ambiguous pronouns was found for the English data set, 𝜒2(1,N=360,284) = 7930.40, p < .001, Cramer’s V = .15, and the Japanesedata set, 𝜒2(1, N=208,428) = 17,300.46, p < .001, Cramer’s V = .29.
Descriptive statistics (Table 1) indicate that there were more masculine pronouns used regardless of language compared to feminine
pronouns. Another Chi-square test was run to see if the numberof masculine (EN = 43,453, JP = 50,350) and non-masculine (EN =167,928, JP = 79,039) pronouns differed between the English andJapanese data sets. A weak, statistically significant relationshipwas found, 𝜒2(1, N= 340,770) = 13,557.2, p < .001, Cramer’s V =.20. Descriptive statistics (Table 1) indicate that use of masculine

CHI EA ’23, April 23–28, 2023, Hamburg, Germany

Katie Seaborn and Yeongdae Kim

Table 2: Descriptive statistics for matching and mismatchingtranslations.
Gender Category

Match

Mismatch

Masculine
Non-masculine
Feminine
Non-feminine

36,164
71,527
16,85590,836

21,475
103,913
2,438
122,950

pronouns increased in the Japanese data set, while use of otherpronouns decreased. Overall, a significant difference in representation between masculine and non-masculine pronouns was found,apparently because of the translation process, supporting H2.

4.3

H3: There will be a masculine bias inpronoun translation from English to
Japanese.

We considered whether the number of masculine (Match = 36,164,
Mismatch = 21,475) and non-masculine (Match = 71,527, Mismatch= 103,913) pronouns differed based on translation mis/matching
between English and Japanese. A Chi-square test revealed a low but
statistically significant relationship between these two variables,𝜒2(1, N = 233,079) = 8,426.7, p < .001, Cramer’s V = .19. As a comparison, we then considered whether the number of feminine (Match= 16,855, Mismatch = 2,438) and non-feminine (Match = 90,836,
Mismatch = 122,950) pronouns based on mis/matching between
Japanese and English. mA Chi-square test revealed a low but statistically significant relationship, 𝜒2(1, N = 233,079) = 14,336.3, p <.001, Cramer’s V = .25. The degree of feminine pronouns comparedto other pronouns, in light of the results for masculine pronouns,
indicates a higher interchange between ambiguous and masculine
pronouns, i.e., a masculine bias, supporting H3. Descriptive statistics (Table 2) indicate that use masculine pronouns decreased, i.e.,
were changed to other pronouns or removed on translation, thus
leading us to reject H3.
We created a confusion matrix (Figure 1) to clarify the interchange of English and Japanese pronouns, i.e., the extent to which

each pronoun or combination of pronouns per sentence matchedon translation. The diagonal values (in green) represent the cases
where English and Japanese matched (only 57% of all pronouns).
The top-right quadrant (in orange) represents the Japanese data,
and the bottom-left quadrant (in purple) represents the English
data. The largest difference was for ambiguous pronouns (English
ambiguous, Japanese none = 73,228). The next largest difference
was between English ambiguous pronouns and (i) Japanese masculine pronouns (n = 11,200) and (ii) Japanese sentences that include
both masculine and ambiguous pronouns (n = 2,324). Both indicate
a masculine bias in the Japanese data set. Comparatively, conversions between ambiguous and (i) feminine and (ii) sentences withfeminine and ambiguous pronouns was only n = 96 and n = 50,
respectively. Feminine pronouns scored the highest, which meansthat feminine pronouns were specified and excluded, while neutral
and masculine pronouns were removed or used interchangeably.
Finally, the matrix visualizes the masculine bias in matched cases
between Japanese and English (n = 21,798).

5

DISCUSSION

Our findings reveal clear gender biases in pronouns for Tatoeba,
notably masculine biases in the English and Japanese data sets.
Moreover, there were mistranslations, and not only with respect toambiguous pronouns. Furthermore, these mistranslations appear
to have been driven by the relative presence and absence of otherpronouns. We discuss these nuances and propose a fix for embracing
pronoun plurality in Tatoeba and other NLP data sets.

5.1

Nuances in Mistranslations: Sources ofMasculine Biases and Pronoun Missteps

A significant number of mismatching pronoun translations alongside an overrepresentation of masculine pronouns was found in
Tatoeba, especially in the Japanese data set. Tatoeba translation
can go either way, so we cannot be sure in which language thesebiases originated; we can only acknowledge the ground truth ofthe crowdsourced translation method itself. Even so, the results
point to an increase in masculine pronouns in the Japanese data set,highlighted against the relatively low levels of feminine pronouns

Figure 1: Confusion matrix showing the distribution of mis/matched pronoun translations. Counts by English pronoun
categories (purple) are contrasted with Japanese pronoun categories (orange); matched cases fall along the diagonal line (green).
Lighter tones indicate lower counts, while darker tones indicate higher counts. None: Non-gendered pronouns, i.e., “it,” names,
and non-humans.

“I’m” Lost in Translation: Pronoun Missteps in Crowdsourced Data Setsand mis/matched translations. One possibility is the presence ofmasculine language biases in Japanese [26]; yet, this is also truefor English [2]. Without research directly comparing Japanese andEnglish outside of translation and this small context of Tatoeba, it
is hard to draw conclusions about whether we should expect either
language to be more masculine-biased than the other. However,the case of ambiguous pronouns provides some nuance. Most were
simply absent in the Japanese data set, likely due to the numberof neutral pronouns such as “I” and “you” in English, which areomitted in Japanese, and “they/them,” which can be a non-binary
singular pronoun or a gender-neutral plural pronoun. Even so, thehigh number of masculine pronouns alongside ambiguous pronouns
in the Japanese data set, where ambiguous pronouns are used in
English, points to translation bias. Nevertheless, we can feel someconfidence in the sheer number of ambiguous pronouns, whichleave open the “genderedness” of language to the reader/listenerand especially allow for queer readings of the text.

5.2

A Pluralistic Approach to Pronoun
Representation in Translated Data Sets

One of the most puzzling aspects of this work, particular to
databases structured like Tatoeba, is the support of plurality forother language matters. Specifically, Tatoeba will repeat sentencesin one or the other language to allow for a plurality of linguistic
expressions, e.g., terms and grammar, and multiple translations ofthat sentence. For example:
Line 49: 生物学は好きになれません。
Line 50: 私は生物学はして好きではありませんでした。
Line 49: I never liked biology.

CHI EA ’23, April 23–28, 2023, Hamburg, Germany

pronouns is being referenced, and so to avoid misgendering that person, a special placeholder referring to certain subset of the pronounlist would be used. Future work can map out a formal specification.
We recognize that this would mean moving away from a plain
text approach to a structured format, which we admit has implications for a project as large as Tatoeba; yet, we argue that, as a matterof inclusion and accuracy, this should be done. Still, we should consider how contributors would make contributions. Practically, it
would be a simple matter of requiring a placeholder for pronouns
rather than a particular pronoun within each sentence. Contributors
could use a generic placeholder, such as [p] (for pronoun), whichadmins, corpus maintainers, and advanced contributors could thenassess and revise, as needed. Still, we cannot expect to inform or
train every contributor in a crowdsourcing initiative about new
structures and rules. A future project could be the development ofan AI-driven interface for inputting sentences into Tatoeba, onethat is trained on the pronoun lists and watches for these pronouns,
and either suggests to the contributor that they be replaced with aplaceholder like [p] or automatically replaces them.

5.3

Limitations

We used one database (Tatoeba) and two languages (English andJapanese). While we provided reasons for this, we urge future
work to evaluate whether and how the findings “translate” to otherdatabases and other languages. Moreover, Tatoeba differs fromother data sets in that the translation can go either way, so we
could not determine the source of mistranslations. Future work willneed to evaluate data sets with a known source language.

6

CONCLUSION

We might think to do the same for pronouns. However, there
are a lot of pronouns, so this seems infeasible; the more lines added
to the database, the more baseline repetition and the greater the
file size of the database. We suggest another approach, drawing
from similar efforts in NLP data sets: standardized labels or tags. In
MASSIVE, for instance, there are tags for “time” and “date,” as in
the example below:

Human language is biased, and so is the human mind. We haverevealed how such biases crop up in translations related to gender through the case study of pronouns in English and Japanese
sentences from Tatoeba, an established, large-scale, open, crowdsourced translation project for NLP and used for training VAs. We
aim to raise awareness, but we have also provided a fix that could
be adopted by Tatoeba and other initiatives. Future work will needto explore the presence and extent of these “translation missteps”
in other data sets and the feasibility of our proposed solution.

{"id": "0", "locale": "en-US", "partition": "test", "scenario": "alarm", "intent": "alarm_set", "utt": "wake me
up at five am this week", "annot_utt": "wake me up at[time : five am] [date : this week]", "worker_id": "1"}This work was funded in part by a Tokyo Tech Young Investigator
Engineering Award.

Line 50: I never liked biology.

Here, the tags are merely annotating the data, rather than acting
as variables that could replace the data. We suggest going one
step further. A pronoun tag or placeholder would take the place ofthe text. More like a variable, it could refer to a list of pronouns
appropriate for the sentence. To rework an example from Tatoeba:Line 6936: 味があるなんてものではなく、もう中なんです。
Line 6936: [p] is not just interested, [p]’s crazy aboutit.
There may be multiple lists depending on the context. For instance,
there may be cases where a famous person or character with known

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