# Resolving Gendered Ambiguous Pronouns with BERT

arXiv: https://arxiv.org/abs/1906.01161 · HTML: https://mlsanity.com/paper/1906.01161/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 16 of 16)

- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation](https://mlsanity.com/paper/2109.03858/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender](https://mlsanity.com/paper/2202.11923/) (2022)
- [They, Them, Theirs: Rewriting with Gender-Neutral English](https://mlsanity.com/paper/2102.06788/) (2021)
- [Choose Your Lenses: Flaws in Gender Bias Evaluation](https://mlsanity.com/paper/2210.11471/) (2022)
- [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://mlsanity.com/paper/2209.04280/) (2022)
- ["I'm" Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets](https://mlsanity.com/paper/2304.13557/) (2023)
- [Stay Together: A System for Single and Split-antecedent Anaphora Resolution](https://mlsanity.com/paper/2104.05320/) (2021)
- [Adapting Coreference Resolution for Processing Violent Death Narratives](https://mlsanity.com/paper/2104.14703/) (2021)
- [RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution](https://mlsanity.com/paper/2309.04977/) (2023)
- [Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction](https://mlsanity.com/paper/2105.12392/) (2021)
- [A Sequence-to-Sequence Approach for Arabic Pronoun Resolution](https://mlsanity.com/paper/2305.11529/) (2023)
- [Impact of Gender Debiased Word Embeddings in Language Modeling](https://mlsanity.com/paper/2105.00908/) (2021)
- [SASICM A Multi-Task Benchmark For Subtext Recognition](https://mlsanity.com/paper/2106.06944/) (2021)
- [Reversing The Twenty Questions Game](https://mlsanity.com/paper/2301.08718/) (2023)

## Abstract (arXiv)

Pronoun resolution is part of coreference resolution, the task of pairing an expression to its referring entity. This is an important task for natural language understanding and a necessary component of machine translation systems, chat bots and assistants. Neural machine learning systems perform far from ideally in this task, reaching as low as 73% F1 scores on modern benchmark datasets. Moreover, they tend to perform better for masculine pronouns than for feminine ones. Thus, the problem is both challenging and important for NLP researchers and practitioners. In this project, we describe our BERT-based approach to solving the problem of gender-balanced pronoun resolution. We are able to reach 92% F1 score and a much lower gender bias on the benchmark dataset shared by Google AI Language team.