@inproceedings{pant-dadu-2022-incorporating,
title = "Incorporating Subjectivity into Gendered Ambiguous Pronoun ({GAP}) Resolution using Style Transfer",
author = "Pant, Kartikey and
Dadu, Tanvi",
editor = "Hardmeier, Christian and
Basta, Christine and
Costa-juss{\`a}, Marta R. and
Stanovsky, Gabriel and
Gonen, Hila",
booktitle = "Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.gebnlp-1.28",
doi = "10.18653/v1/2022.gebnlp-1.28",
pages = "273--281",
abstract = "The GAP dataset is a Wikipedia-based evaluation dataset for gender bias detection in coreference resolution, containing mostly objective sentences. Since subjectivity is ubiquitous in our daily texts, it becomes necessary to evaluate models for both subjective and objective instances. In this work, we present a new evaluation dataset for gender bias in coreference resolution, GAP-Subjective, which increases the coverage of the original GAP dataset by including subjective sentences. We outline the methodology used to create this dataset. Firstly, we detect objective sentences and transfer them into their subjective variants using a sequence-to-sequence model. Secondly, we outline the thresholding techniques based on fluency and content preservation to maintain the quality of the sentences. Thirdly, we perform automated and human-based analysis of the style transfer and infer that the transferred sentences are of high quality. Finally, we benchmark both GAP and GAP-Subjective datasets using a BERT-based model and analyze its predictive performance and gender bias.",
}
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<abstract>The GAP dataset is a Wikipedia-based evaluation dataset for gender bias detection in coreference resolution, containing mostly objective sentences. Since subjectivity is ubiquitous in our daily texts, it becomes necessary to evaluate models for both subjective and objective instances. In this work, we present a new evaluation dataset for gender bias in coreference resolution, GAP-Subjective, which increases the coverage of the original GAP dataset by including subjective sentences. We outline the methodology used to create this dataset. Firstly, we detect objective sentences and transfer them into their subjective variants using a sequence-to-sequence model. Secondly, we outline the thresholding techniques based on fluency and content preservation to maintain the quality of the sentences. Thirdly, we perform automated and human-based analysis of the style transfer and infer that the transferred sentences are of high quality. Finally, we benchmark both GAP and GAP-Subjective datasets using a BERT-based model and analyze its predictive performance and gender bias.</abstract>
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%0 Conference Proceedings
%T Incorporating Subjectivity into Gendered Ambiguous Pronoun (GAP) Resolution using Style Transfer
%A Pant, Kartikey
%A Dadu, Tanvi
%Y Hardmeier, Christian
%Y Basta, Christine
%Y Costa-jussà, Marta R.
%Y Stanovsky, Gabriel
%Y Gonen, Hila
%S Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F pant-dadu-2022-incorporating
%X The GAP dataset is a Wikipedia-based evaluation dataset for gender bias detection in coreference resolution, containing mostly objective sentences. Since subjectivity is ubiquitous in our daily texts, it becomes necessary to evaluate models for both subjective and objective instances. In this work, we present a new evaluation dataset for gender bias in coreference resolution, GAP-Subjective, which increases the coverage of the original GAP dataset by including subjective sentences. We outline the methodology used to create this dataset. Firstly, we detect objective sentences and transfer them into their subjective variants using a sequence-to-sequence model. Secondly, we outline the thresholding techniques based on fluency and content preservation to maintain the quality of the sentences. Thirdly, we perform automated and human-based analysis of the style transfer and infer that the transferred sentences are of high quality. Finally, we benchmark both GAP and GAP-Subjective datasets using a BERT-based model and analyze its predictive performance and gender bias.
%R 10.18653/v1/2022.gebnlp-1.28
%U https://aclanthology.org/2022.gebnlp-1.28
%U https://doi.org/10.18653/v1/2022.gebnlp-1.28
%P 273-281
Markdown (Informal)
[Incorporating Subjectivity into Gendered Ambiguous Pronoun (GAP) Resolution using Style Transfer](https://aclanthology.org/2022.gebnlp-1.28) (Pant & Dadu, GeBNLP 2022)
ACL