@inproceedings{ionita-etal-2019-resolving,
title = "Resolving Gendered Ambiguous Pronouns with {BERT}",
author = "Ionita, Matei and
Kashnitsky, Yury and
Krige, Ken and
Larin, Vladimir and
Atanasov, Atanas and
Logvinenko, Dennis",
editor = "Costa-juss{\`a}, Marta R. and
Hardmeier, Christian and
Radford, Will and
Webster, Kellie",
booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3817",
doi = "10.18653/v1/W19-3817",
pages = "113--119",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Resolving Gendered Ambiguous Pronouns with BERT
%A Ionita, Matei
%A Kashnitsky, Yury
%A Krige, Ken
%A Larin, Vladimir
%A Atanasov, Atanas
%A Logvinenko, Dennis
%Y Costa-jussà, Marta R.
%Y Hardmeier, Christian
%Y Radford, Will
%Y Webster, Kellie
%S Proceedings of the First Workshop on Gender Bias in Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F ionita-etal-2019-resolving
%X 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.
%R 10.18653/v1/W19-3817
%U https://aclanthology.org/W19-3817
%U https://doi.org/10.18653/v1/W19-3817
%P 113-119
Markdown (Informal)
[Resolving Gendered Ambiguous Pronouns with BERT](https://aclanthology.org/W19-3817) (Ionita et al., GeBNLP 2019)
ACL
- Matei Ionita, Yury Kashnitsky, Ken Krige, Vladimir Larin, Atanas Atanasov, and Dennis Logvinenko. 2019. Resolving Gendered Ambiguous Pronouns with BERT. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 113–119, Florence, Italy. Association for Computational Linguistics.