aieng-lab/genter-ajibawa

GENTER (GEnder Name TEmplates with pRonouns) Ajibawa

This dataset consists of template Ajibawa-derived sentences associating first names ([NAME]) with third-person singular pronouns ([PRONOUN]). Unlike the original GENTER dataset based on BookCorpus, that could not directly be published due to licencing issues, this equivalently derived dataset provides easy access while maintaining the same generation principles as the original GENTER properties.

Usage

from datasets import load_dataset
genter = load_dataset('aieng-lab/genter')

Dataset Details

Dataset Description

This dataset is a filtered version of Ajibawa General Stories Collection that includes only sentences where a first name appears alongside its correct third-person singular pronoun (he/she).

From these sentences, template-based sentences (masked) are created with two template keys: [NAME] and [PRONOUN]. This design allows the dataset to generate diverse sentences by varying the names (e.g., using names from aieng-lab/namexact) and inserting the appropriate pronoun for each name.

Dataset Sources

• Repository: github.com/aieng-lab/gradiend-bias
• Paper:
• Original Data: Ajibawa General Stories Collection

Dataset Structure

• text: the original entry of BookCorpus
• masked: the masked version of text, i.e., with template masks for the name ([NAME]) and the pronoun ([PRONOUN])
• label: the gender of the original used name (F for female and M for male)
• name: the original name in text that is masked in masked as [NAME]
• pronoun: the original pronoun in text that is masked in masked as [PRONOUN] (he/ she)
• pronoun_count: the number of occurrences of pronouns (typically 1, at most 4)
• index: The index of text in Ajibawa General Stories Collection

Examples:

index	text	masked	label	name	pronoun	pronoun_count
71130173	jessica asked , not sounding as if she cared about the answer .	[NAME] asked , not sounding as if [PRONOUN] cared about the answer .	F	jessica	she	1
17316262	jeremy looked around and there were many people at the campsite ; then he looked down at the small keg .	[NAME] looked around and there were many people at the campsite ; then [PRONOUN] looked down at the small keg .	M	jeremy	he	1
41606581	tabitha did n't seem to notice as she swayed to the loud , thrashing music .	[NAME] did n't seem to notice as [PRONOUN] swayed to the loud , thrashing music .	F	tabitha	she	1
52926749	gerald could come in now , have a look if he wanted .	[NAME] could come in now , have a look if [PRONOUN] wanted .	M	gerald	he	1
47875293	chapter six as time went by , matthew found that he was no longer certain that he cared for journalism .	chapter six as time went by , [NAME] found that [PRONOUN] was no longer certain that [PRONOUN] cared for journalism .	M	matthew	he	2
73605732	liam tried to keep a straight face , but he could n't hold back a smile .	[NAME] tried to keep a straight face , but [PRONOUN] could n't hold back a smile .	M	liam	he	1
31376791	after all , ella was the same as she 'd always been .	after all , [NAME] was the same as [PRONOUN] 'd always been .	F	ella	she	1
61942082	seth shrugs as he hops off the bed and lands on the floor with a thud .	[NAME] shrugs as [PRONOUN] hops off the bed and lands on the floor with a thud .	M	seth	he	1
68696573	graham 's eyes meet mine , but i 'm sure there 's no way he remembers what he promised me several hours ago until he stands , stretching .	[NAME] 's eyes meet mine , but i 'm sure there 's no way [PRONOUN] remembers what [PRONOUN] promised me several hours ago until [PRONOUN] stands , stretching .	M	graham	he	3
28923447	grief tore through me-the kind i had n't known would be possible to feel again , because i had felt this when i 'd held caleb as he died .	grief tore through me-the kind i had n't known would be possible to feel again , because i had felt this when i 'd held [NAME] as [PRONOUN] died .	M	caleb	he	1

Dataset Creation

Curation Rationale

For the training of a gender bias GRADIEND model, a diverse dataset associating first names with both, its factual and counterfactual pronoun associations, to assess gender-related gradient information.

Source Data

The dataset is derived from Ajibawa General Stories Collection by filtering it and extracting the template structure.

We selected Ajibawa General Stories Collection as foundational dataset due to its focus on fictional narratives where characters are often referred to by their first names (and as it provides better licensing as BookCorpus). In contrast, the English Wikipedia, also commonly used for the training of transformer models, was less suitable for our purposes. For instance, sentences like [NAME] Jackson was a musician, [PRONOUN] was a great singer may be biased towards the name Michael.

Data Collection and Processing

We filter the entries of BookCorpus and include only sentences that meet the following criteria:

• Each sentence contains at least 50 characters
• Exactly one name of aieng-lab/namexact is contained, ensuringa correct name match.
• No other names from a larger name dataset (aieng-lab/namextend) are included, ensuring that only a single name appears in the sentence.
• The correct name's gender-specific third-person pronoun (he or she) is included at least once.
• All occurrences of the pronoun appear after the name in the sentence.
• The counterfactual pronoun does not appear in the sentence.
• The sentence excludes gender-specific reflexive pronouns (himself, herself) and possesive pronouns (his, her, him, hers)
• Gendered nouns (e.g., actor, actress, ...) are excluded, based on a gendered-word dataset with 2421 entries.

This approach generated a total of 83772 sentences. To further enhance data quality, we employed s imple BERT model (bert-base-uncased) as a judge model. This model must predict the correct pronoun for selected names with high certainty, otherwise, sentences may contain noise or ambiguous terms not caught by the initial filtering. Specifically, we used 50 female and 50 male names from the (aieng-lab/namextend) train split, and a correct prediction means the correct pronoun token is predicted as the token with the highest probability in the induced Masked Language Modeling (MLM) task. Only sentences for which the judge model correctly predicts the pronoun for every test case were retrained, resulting in a total of 27031 sentences.

The data is split into training (87.5%), validation (2.5%) and test (10%) subsets.

Citation

BibTeX:

@inproceedings{drechsel2026gradiend,
  title     = {{GRADIEND}: Feature Learning within Neural Networks Exemplified through Biases},
  author    = {Drechsel, Jonathan and Herbold, Steffen},
  booktitle = {Proceedings of the International Conference on Learning Representations},
  year      = {2026},
  url       = {https://arxiv.org/abs/2502.01406}
}

Dataset Card Authors

jdrechsel