# Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

arXiv: https://arxiv.org/abs/2202.11923 · HTML: https://mlsanity.com/paper/2202.11923/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Measuring Bias in Contextualized Word Representations](https://mlsanity.com/paper/1906.07337/) (2019)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Identifying and Reducing Gender Bias in Word-Level Language Models](https://mlsanity.com/paper/1904.03035/) (2019)
- [RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models](https://mlsanity.com/paper/2106.03521/) (2021)
- [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://mlsanity.com/paper/1912.11078/) (2019)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021)
- [On Measuring and Mitigating Biased Inferences of Word Embeddings](https://mlsanity.com/paper/1908.09369/) (2019)
- [Sustainable Modular Debiasing of Language Models](https://mlsanity.com/paper/2109.03646/) (2021)
- [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021)
- [A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces](https://mlsanity.com/paper/1909.06092/) (2019)
- [Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors](https://mlsanity.com/paper/1904.11783/) (2019)
- [DS-TOD: Efficient Domain Specialization for Task Oriented Dialog](https://mlsanity.com/paper/2110.08395/) (2021)
- [Resolving Gendered Ambiguous Pronouns with BERT](https://mlsanity.com/paper/1906.01161/) (2019)
- [On the Importance of Delexicalization for Fact Verification](https://mlsanity.com/paper/1909.09868/) (2019)

## Built on by (most cited first, 30 of 47)

- ["I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation](https://mlsanity.com/paper/2305.09941/) (2023)
- [Perturbation Augmentation for Fairer NLP](https://mlsanity.com/paper/2205.12586/) (2022)
- [MISGENDERED: Limits of Large Language Models in Understanding Pronouns](https://mlsanity.com/paper/2306.03950/) (2023)
- [Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog](https://mlsanity.com/paper/2205.10400/) (2022)
- [GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models](https://mlsanity.com/paper/2205.12247/) (2022)
- [Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models](https://mlsanity.com/paper/2305.17072/) (2023)
- [Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges](https://mlsanity.com/paper/2301.10075/) (2023)
- [AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters](https://mlsanity.com/paper/2401.06408/) (2024)
- [Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP](https://mlsanity.com/paper/2405.17159/) (2024)
- [Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus](https://mlsanity.com/paper/2310.05294/) (2023)
- [What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns](https://mlsanity.com/paper/2305.16051/) (2023)
- [Fair and Argumentative Language Modeling for Computational Argumentation](https://mlsanity.com/paper/2204.04026/) (2022)
- [Robust Pronoun Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?](https://mlsanity.com/paper/2404.03134/) (2024)
- [How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns](https://mlsanity.com/paper/2204.10281/) (2022)
- [Fairness in Language Models Beyond English: Gaps and Challenges](https://mlsanity.com/paper/2302.12578/) (2023)
- [Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies](https://mlsanity.com/paper/2312.11779/) (2023)
- [Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research](https://mlsanity.com/paper/2310.13915/) (2023)
- [A Prompt Response to the Demand for Automatic Gender-Neutral Translation](https://mlsanity.com/paper/2402.06041/) (2024)
- [Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers](https://mlsanity.com/paper/2210.07362/) (2022)
- [WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case](https://mlsanity.com/paper/2409.05653/) (2024)
- [SocioProbe: What, When, and Where Language Models Learn about Sociodemographics](https://mlsanity.com/paper/2211.04281/) (2022)
- [Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models](https://mlsanity.com/paper/2405.08477/) (2024)
- [Agree to Disagree? A Meta-Evaluation of LLM Misgendering](https://mlsanity.com/paper/2504.17075/) (2025)
- [Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES](https://mlsanity.com/paper/2310.19345/) (2023)
- [MisgenderMender: A Community-Informed Approach to Interventions for Misgendering](https://mlsanity.com/paper/2404.14695/) (2024)
- [Bridging Fairness and Environmental Sustainability in Natural Language Processing](https://mlsanity.com/paper/2211.04256/) (2022)
- [Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts](https://mlsanity.com/paper/2304.12810/) (2023)
- [QueerBench: Quantifying Discrimination in Language Models Toward Queer Identities](https://mlsanity.com/paper/2406.12399/) (2024)
- [Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs](https://mlsanity.com/paper/2409.13852/) (2024)
- ["I'm" Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets](https://mlsanity.com/paper/2304.13557/) (2023)

## Abstract (arXiv)

The world of pronouns is changing. From a closed class of words with few members to a much more open set of terms to reflect identities. However, Natural Language Processing (NLP) is barely reflecting this linguistic shift, even though recent work outlined the harms of gender-exclusive language technology. Particularly problematic is the current modeling 3rd person pronouns, as it largely ignores various phenomena like neopronouns, i.e., pronoun sets that are novel and not (yet) widely established. This omission contributes to the discrimination of marginalized and underrepresented groups, e.g., non-binary individuals. However, other identity-expression phenomena beyond gender are also ignored by current NLP technology. In this paper, we provide an overview of 3rd person pronoun issues for NLP. Based on our observations and ethical considerations, we define a series of desiderata for modeling pronouns in language technology. We evaluate existing and novel modeling approaches w.r.t. these desiderata qualitatively, and quantify the impact of a more discrimination-free approach on established benchmark data.