---
title: "Counter-GAP — paper by Xie et al. (2023) — summary and ideas"
description: "Coreference datasets that measure gender bias usually have one of two weaknesses. 39 references, cited by 4 papers here."
authors: ["Zhongbin Xie", "Vid Kocijan", "Thomas Lukasiewicz", "Oana-Maria Camburu"]
year: 2023
venue: "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics"
source: "https://aclanthology.org/2023.eacl-main.272/"
doi: "10.18653/v1/2023.eacl-main.272"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xie-2023-counter-gap-counterfactual-bias-evaluation/"
---
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# Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns

**Authors:** Zhongbin Xie, [Vid Kocijan](https://md.research.elamur.ai/free/en/authors/vid-kocijan/), [Thomas Lukasiewicz](https://md.research.elamur.ai/free/en/authors/thomas-lukasiewicz/) and [Oana-Maria Camburu](https://md.research.elamur.ai/free/en/authors/oana-maria-camburu/) · **Year:** 2023 · **Venue:** [Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2023](https://md.research.elamur.ai/free/en/venues/eacl/2023/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.eacl-main.272/) · [PDF](https://aclanthology.org/2023.eacl-main.272.pdf) · [DOI 10.18653/v1/2023.eacl-main.272](https://doi.org/10.18653/v1/2023.eacl-main.272) · [OpenAlex](https://openalex.org/W4386566571)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [4](https://md.research.elamur.ai/free/en/papers/xie-2023-counter-gap-counterfactual-bias-evaluation/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Coreference datasets that measure gender bias usually have one of two weaknesses. Some are built from hand-written sentences, as WinoBias and WinoGender are. Others, like GAP, collect real text but cannot pair masculine and feminine cases. In GAP, the feminine items happen to be harder, so a score gap may reflect difficulty rather than bias. The authors address this with counterfactual generation. They take natural passages containing an ambiguous gendered pronoun and rewrite each one into several versions that differ only in gender. The result, Counter-GAP, groups these versions into annotated quadruples built from fiction books.

The authors also show that averaging scores by gender group can hide bias, because errors in opposite directions offset each other. They call this bias cancellation. Their replacement metric, ∆I, works per quadruple. It compares how often predictions change when gender is swapped against how often they change when only names are reshuffled within a gender.

All four tested pre-trained models flip their predictions much more across genders than within genders. Of the two debiasing methods, name-based CDA reduces this bias better than anonymization-based CDA. The study matters because it measures bias on natural text with a cleanly isolated gender variable.

## Key points
- **Source and pipeline:**
  - Text comes from 1575 fiction books in Project Gutenberg and BookCorpus.
  - spaCy finds names and pronouns; the gender-guesser package assigns name gender.
  - A context is limited to five sentences or fewer, and it must include precisely two feminine names and two masculine names.
- **Filtering:** The three GAP patterns (FinalPro, MedialPro, InitialPro) select ambiguous pronouns. This yields 2585 original contexts.
- **Quadruple structure:**
  - The original passage.
  - A gender-controlled copy, in which same-gender names trade places.
  - Two gender-swapped copies, in which gendered words follow an extended list from Zhao et al. and names are exchanged across genders.
- **Final dataset and quality control:** Counter-GAP has 4008 instances in 1002 quadruples. Annotators drop items that contradict commonsense. A whole quadruple is discarded if its four majority labels disagree.
- **Stated limitations:**
  - Roughly half of the text is historical fiction.
  - Gender is treated as binary.
  - The rules are specific to English.
  - The dataset is diagnostic only: finding no bias does not prove a model is fair.

## Interesting ideas
- Add a "same-type" perturbation as a baseline next to the "cross-type" one, so that the noise caused by merely changing surface tokens is subtracted from the effect being measured.
- Score consistency per matched group of variants rather than comparing group averages, because averaged differences can cancel each other and hide real errors.
- Build counterfactuals by permuting entities already in the passage rather than inserting new ones, which keeps the text natural and keeps every name plausible in its context.
- Keep a set of rewritten variants only when every variant receives the same gold answer, so items whose meaning broke during rewriting are discarded automatically.
- Mine hard pronoun cases from real text using syntactic position patterns, such as a pronoun in an initial subordinate clause with two same-gender candidate names nearby.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Evaluation runs could pair every meaningful edit with a neutral edit of the same size, turning the neutral edit into a built-in negative control.
- **I2** (learn): Regression tests for the parser and the readers could report flip rates per variant set rather than overall accuracy deltas.
- **I3** (frames): Coreference and semantic-role checks could swap the participants of a story and verify that the who-did-what-to-whom frames swap with them.
- **I4** (learn): Silver data built from paraphrased or swapped sentences could require agreement across all variants before an item enters training.
- **I5** (frames): Rules over the UD tree could harvest genuinely ambiguous pronouns from tales and fables to stress-test the coreference module.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [TU Wien](https://md.research.elamur.ai/free/en/universities/tu-wien/) — University, Vienna, Austria · 7 works · 786 citations

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## Cite

Cite as: Zhongbin Xie, Vid Kocijan, Thomas Lukasiewicz and Oana-Maria Camburu. 2023. Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xie2023counter,
  title = {{Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns}},
  author = {Zhongbin Xie, Vid Kocijan, Thomas Lukasiewicz and Oana-Maria Camburu},
  booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.18653/v1/2023.eacl-main.272},
  url = {https://doi.org/10.18653/v1/2023.eacl-main.272}
}
```

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*Updated 1 November 2026*