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Neural Machine Translation systems built on top of Transformer-based architectures are routinely improving the state-of-the-art in translation quality according to word-overlap metrics.
Intrinsic bias metrics do not correlate with application bias
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, and Adam Lopez. 2021 · 1940
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Attention is All you Need
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Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
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Getting gender right in neural machine translation
Eva Vanmassenhove, Christian Hardmeier, and Andy Way. 2018 · 2018
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Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018a · 2018
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Equalizing gender bias in neural machine translation with word embeddings techniques
Joel Escudé Font and Marta R. Costa-jussà. 2019 · 2019
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Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
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Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019 · 2019
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Gender Bias in Contextualized Word Embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang. 2019 · 2019
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Towards mitigating gender bias in a decoder-based neural machine translation model by adding contextual information
Christine Basta, Marta R. Costa-jussà, and José A. R. Fonollosa. 2020 · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Fine-tuning neural machine translation on gender-balanced datasets
Marta R. Costa-jussà and Adrià de Jorge. 2020 · 2020
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The unreasonable volatility of neural machine translation models
Marzieh Fadaee and Christof Monz. 2020 · 2020
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Fair is better than sensational: Man is to doctor as woman is to doctor
Malvina Nissim, Rik van Noord, and Rob van der Goot. 2020 · 2020
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Assessing gender bias in machine translation: a case study with google translate
Marcelo OR Prates, Pedro H Avelar, and Luís C Lamb. 2020 · 2020
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Word alignment by fine-tuning embeddings on parallel corpora
Zi-Yi Dou and Graham Neubig. 2021 · 2021
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Beyond english-centric multilingual machine translation
Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Michael Auli, and Armand Joulin. 2021 · 2021
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Debiasing pre-trained contextualised embeddings
Masahiro Kaneko and Danushka Bollegala. 2021 · 2021
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Collecting a large-scale gender bias dataset for coreference resolution and machine translation
Shahar Levy, Koren Lazar, and Gabriel Stanovsky. 2021 · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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Stanza: A python natural language processing toolkit for many human languages
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Neural machine translation doesn’t translate gender coreference right unless you make it
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OPUS-MT – building open translation services for the world
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Man is to Computer Programmer as Woman is to Homemaker ? debiasing Word Embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018b
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Gender bias amplification during speed-quality optimization in neural machine translation
Adithya Renduchintala, Denise Diaz, Kenneth Heafield, Xian Li, and Mona Diab. 2021 · 2021
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Evaluating gender bias in natural language inference
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The paradox of the compositionality of natural language: A neural machine translation case study
Verna Dankers, Elia Bruni, and Dieuwke Hupkes. 2022 · 2022
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Investigating failures of automatic translationin the case of unambiguous gender
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