# "I'm" Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets

arXiv: https://arxiv.org/abs/2304.13557 · HTML: https://mlsanity.com/paper/2304.13557/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 22 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [Multi-Dimensional Gender Bias Classification](https://mlsanity.com/paper/2005.00614/) (2020)
- [On Measuring Gender Bias in Translation of Gender-neutral Pronouns](https://mlsanity.com/paper/1905.11684/) (2019)
- [Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender](https://mlsanity.com/paper/2202.11923/) (2022)
- [Unsupervised Discovery of Implicit Gender Bias](https://mlsanity.com/paper/2004.08361/) (2020)
- [The State of Speech in HCI: Trends, Themes and Challenges](https://mlsanity.com/paper/1810.06828/) (2018)
- [Resolving Gendered Ambiguous Pronouns with BERT](https://mlsanity.com/paper/1906.01161/) (2019)
- [What Pronouns for Pepper? A Critical Review of Gender/ing in Research](https://mlsanity.com/paper/2212.04206/) (2022)

## Built on by (most cited first, 4 of 4)

- [Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP](https://mlsanity.com/paper/2404.19071/) (2024)
- [Silver-Tongued and Sundry: Exploring Intersectional Pronouns with ChatGPT](https://mlsanity.com/paper/2405.08238/) (2024)
- [Coimagining the Future of Voice Assistants with Cultural Sensitivity](https://mlsanity.com/paper/2403.17599/) (2024)
- [Inter(sectional) Alia(s): Ambiguity in Voice Agent Identity via Intersectional Japanese Self-Referents](https://mlsanity.com/paper/2506.01998/) (2025)

## Abstract (arXiv)

As virtual assistants continue to be taken up globally, there is an ever-greater need for these speech-based systems to communicate naturally in a variety of languages. Crowdsourcing initiatives have focused on multilingual translation of big, open data sets for use in natural language processing (NLP). Yet, language translation is often 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 found that masculine pronoun biases were present overall, even though plurality 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 and offer a practical solution to embed plurality in NLP data sets.