E-VAN : Enhanced Variational AutoEncoder Network for Mitigating Gender Bias in Static Word Embeddings
Pages 57 - 64
Abstract
Recent research has shown that pre-trained context-independent word embeddings display biases such as racial bias, gender bias, etc. Using a novel, tunable algorithm, this study attempts to mitigate the hidden gender bias in static embeddings. In order to train the model, an enhanced variational autoencoder (E-VAN) is used to learn the latent space of the embedding. Then the latent distributions are used while adaptively resampling and re-weighting the rare/under-represented data. While the word embeddings retain semantic information, E-VAN effectively mitigates unwanted biased gendered associations. Our method E-VAN outperforms previous state-of-the-art methods in both quantitative and human evaluation.
References
[1]
Balazs Aczel, Bence Bagó, Andrei Foldes, and Aba Szollosi. 2013. Measuring individual differences in cognitive biases. In Proceedings of the 35th Annual Meeting of the Cognitive Science Society, CogSci 2013, Berlin, Germany, July 31 - August 3, 2013, Markus Knauff, Michael Pauen, Natalie Sebanz, and Ipke Wachsmuth (Eds.). cognitivesciencesociety.org. https://mindmodeling.org/cogsci2013/papers/0684/index.html
[2]
Martín Arjovsky and Léon Bottou. 2017. Towards Principled Methods for Training Generative Adversarial Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=Hk4_qw5xe
[3]
Avrim Blum, Nika Haghtalab, and Ariel D. Procaccia. 2015. Variational Dropout and the Local Reparameterization Trick. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett (Eds.). 2575–2583. https://proceedings.neurips.cc/paper/2015/hash/bc7316929fe1545bf0b98d114ee3ecb8-Abstract.html
[4]
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomás Mikolov. 2017. Enriching Word Vectors with Subword Information. Trans. Assoc. Comput. Linguistics 5 (2017), 135–146. https://doi.org/10.1162/tacl_a_00051
[5]
Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam Tauman Kalai. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 4349–4357. https://proceedings.neurips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html
[6]
Daniel de Vassimon Manela, David Errington, Thomas Fisher, Boris van Breugel, and Pasquale Minervini. 2021. Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, Paola Merlo, Jörg Tiedemann, and Reut Tsarfaty (Eds.). Association for Computational Linguistics, 2232–2242. https://doi.org/10.18653/v1/2021.eacl-main.190
[7]
Sunipa Dev and Jeff M. Phillips. 2019. Attenuating Bias in Word vectors. In The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan(Proceedings of Machine Learning Research, Vol. 89), Kamalika Chaudhuri and Masashi Sugiyama (Eds.). PMLR, 879–887. http://proceedings.mlr.press/v89/dev19a.html
[8]
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell. 2017. Adversarial Feature Learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=BJtNZAFgg
[9]
Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. 2010. Why Does Unsupervised Pre-training Help Deep Learning?J. Mach. Learn. Res. 11(2010), 625–660. https://doi.org/10.5555/1756006.1756025
[10]
Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001. Placing search in context: the concept revisited. In Proceedings of the Tenth International World Wide Web Conference, WWW 10, Hong Kong, China, May 1-5, 2001, Vincent Y. Shen, Nobuo Saito, Michael R. Lyu, and Mary Ellen Zurko (Eds.). ACM, 406–414. https://doi.org/10.1145/371920.372094
[11]
Scott E. Friedman, Sonja Schmer-Galunder, Anthony Chen, Robert P. Goldman, and Michelle Ausman. 2020. Gender Gaps Correlate with Gender Bias in Social Media Word Embeddings. In Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020, Stephanie Denison, Michael Mack, Yang Xu, and Blair C. Armstrong (Eds.). cognitivesciencesociety.org. https://cogsci.mindmodeling.org/2020/papers/0621/index.html
[12]
Debasis Ganguly, Dwaipayan Roy, Mandar Mitra, and Gareth J. F. Jones. 2015. Word Embedding based Generalized Language Model for Information Retrieval. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 9-13, 2015, Ricardo Baeza-Yates, Mounia Lalmas, Alistair Moffat, and Berthier A. Ribeiro-Neto (Eds.). ACM, 795–798. https://doi.org/10.1145/2766462.2767780
[13]
Hila Gonen and Yoav Goldberg. 2019. Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them. In Proceedings of the 2019 Workshop on Widening NLP@ACL 2019, Florence, Italy, July 28, 2019, Amittai Axelrod, Diyi Yang, Rossana Cunha, Samira Shaikh, and Zeerak Waseem (Eds.). Association for Computational Linguistics, 60–63. https://aclanthology.org/W19-3621/
[14]
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 2672–2680. https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html
[15]
Guy Halawi, Gideon Dror, Evgeniy Gabrilovich, and Yehuda Koren. 2012. Large-scale learning of word relatedness with constraints. In The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, Beijing, China, August 12-16, 2012, Qiang Yang, Deepak Agarwal, and Jian Pei (Eds.). ACM, 1406–1414. https://doi.org/10.1145/2339530.2339751
[16]
Felix Hill, Roi Reichart, and Anna Korhonen. 2014. Multi-Modal Models for Concrete and Abstract Concept Meaning. Trans. Assoc. Comput. Linguistics 2 (2014), 285–296. https://doi.org/10.1162/tacl_a_00183
[17]
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, and Hanna M. Wallach. 2019. Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, Stephen A. Brewster, Geraldine Fitzpatrick, Anna L. Cox, and Vassilis Kostakos (Eds.). ACM, 600. https://doi.org/10.1145/3290605.3300830
[18]
Aylin Caliskan Islam, Joanna J. Bryson, and Arvind Narayanan. 2016. Semantics derived automatically from language corpora necessarily contain human biases. CoRR abs/1608.07187(2016). arXiv:1608.07187http://arxiv.org/abs/1608.07187
[19]
Sophie Jentzsch, Patrick Schramowski, Constantin Rothkopf, and Kristian Kersting. 2019. Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19). Association for Computing Machinery, New York, NY, USA, 37–44. https://doi.org/10.1145/3306618.3314267
[20]
David Jurgens, Saif M. Mohammad, Peter D. Turney, and Keith J. Holyoak. 2012. SemEval-2012 Task 2: Measuring Degrees of Relational Similarity. In Proceedings of the 6th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2012, Montréal, Canada, June 7-8, 2012, Eneko Agirre, Johan Bos, and Mona T. Diab (Eds.). The Association for Computer Linguistics, 356–364. https://aclanthology.org/S12-1047/
[21]
Masahiro Kaneko and Danushka Bollegala. 2019. Gender-preserving Debiasing for Pre-trained Word Embeddings. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 1641–1650. https://doi.org/10.18653/v1/p19-1160
[22]
Diederik P. Kingma. 2013. Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form. CoRR abs/1306.0733(2013). arXiv:1306.0733http://arxiv.org/abs/1306.0733
[23]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980
[24]
Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. 2014. Semi-supervised Learning with Deep Generative Models. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 3581–3589. https://proceedings.neurips.cc/paper/2014/hash/d523773c6b194f37b938d340d5d02232-Abstract.html
[25]
Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, and Max Welling. 2016. Improving Variational Autoencoders with Inverse Autoregressive Flow. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 4736–4744. https://proceedings.neurips.cc/paper/2016/hash/ddeebdeefdb7e7e7a697e1c3e3d8ef54-Abstract.html
[26]
Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1312.6114
[27]
Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017. End-to-end Neural Coreference Resolution. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017, Martha Palmer, Rebecca Hwa, and Sebastian Riedel (Eds.). Association for Computational Linguistics, 188–197. https://doi.org/10.18653/v1/d17-1018
[28]
Kenton Lee, Luheng He, and Luke Zettlemoyer. 2018. Higher-Order Coreference Resolution with Coarse-to-Fine Inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers), Marilyn A. Walker, Heng Ji, and Amanda Stent (Eds.). Association for Computational Linguistics, 687–692. https://doi.org/10.18653/v1/n18-2108
[29]
Omer Levy and Yoav Goldberg. 2014. Linguistic Regularities in Sparse and Explicit Word Representations. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning, CoNLL 2014, Baltimore, Maryland, USA, June 26-27, 2014, Roser Morante and Wen-tau Yih (Eds.). ACL, 171–180. https://doi.org/10.3115/v1/w14-1618
[30]
Thang Luong, Richard Socher, and Christopher D. Manning. 2013. Better Word Representations with Recursive Neural Networks for Morphology. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning, CoNLL 2013, Sofia, Bulgaria, August 8-9, 2013, Julia Hockenmaier and Sebastian Riedel (Eds.). ACL, 104–113. https://aclanthology.org/W13-3512/
[31]
Thomas Manzini, Yao Chong Lim, Yulia Tsvetkov, and Alan W. Black. 2019. Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings. CoRR abs/1904.04047(2019). arXiv:1904.04047http://arxiv.org/abs/1904.04047
[32]
Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1301.3781
[33]
Tomás Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013. Linguistic Regularities in Continuous Space Word Representations. In Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 9-14, 2013, Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA, Lucy Vanderwende, Hal Daumé III, and Katrin Kirchhoff (Eds.). The Association for Computational Linguistics, 746–751. https://aclanthology.org/N13-1090/
[34]
Ajinkya More. 2016. Survey of resampling techniques for improving classification performance in unbalanced datasets. CoRR abs/1608.06048(2016). arXiv:1608.06048http://arxiv.org/abs/1608.06048
[35]
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, Alessandro Moschitti, Bo Pang, and Walter Daelemans (Eds.). ACL, 1532–1543. https://doi.org/10.3115/v1/d14-1162
[36]
Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A. Smith, and Yejin Choi. 2018. Event2Mind: Commonsense Inference on Events, Intents, and Reactions. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, Iryna Gurevych and Yusuke Miyao (Eds.). Association for Computational Linguistics, 463–473. https://doi.org/10.18653/v1/P18-1043
[37]
Dan Roth and Wen-tau Yih. 2004. A Linear Programming Formulation for Global Inference in Natural Language Tasks. In Proceedings of the Eighth Conference on Computational Natural Language Learning, CoNLL 2004, Held in cooperation with HLT-NAACL 2004, Boston, Massachusetts, USA, May 6-7, 2004, Hwee Tou Ng and Ellen Riloff (Eds.). ACL, 1–8. https://aclanthology.org/W04-2401/
[38]
Herbert Rubenstein and John B. Goodenough. 1965. Contextual correlates of synonymy. Commun. ACM 8, 10 (1965), 627–633. https://doi.org/10.1145/365628.365657
[39]
Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, and Kush R. Varshney. 2019. Fairness GAN: Generating datasets with fairness properties using a generative adversarial network. IBM J. Res. Dev. 63, 4/5 (2019), 3:1–3:9. https://doi.org/10.1147/JRD.2019.2945519
[40]
Ben Schmidt. 2015. Rejecting the gender binary: a vector-space operation. Ben’s Bookworm Blog(2015).
[41]
Bei Shi, Zihao Fu, Lidong Bing, and Wai Lam. 2018. Learning Domain-Sensitive and Sentiment-Aware Word Embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, Iryna Gurevych and Yusuke Miyao (Eds.). Association for Computational Linguistics, 2494–2504. https://doi.org/10.18653/v1/P18-1232
[42]
John P. Sullins. 1999. Artificial knowing: gender and the thinking machine. SIGCAS Comput. Soc. 29, 1 (1999), 47–48. https://doi.org/10.1145/382042.382053
[43]
Zhisheng Xiao, Qing Yan, and Yali Amit. 2020. Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https://proceedings.neurips.cc/paper/2020/hash/eddea82ad2755b24c4e168c5fc2ebd40-Abstract.html
[44]
Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, and Daniel Cer. 2021. Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 2: Short Papers), Virtual Event, August 1-6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 263–268. https://doi.org/10.18653/v1/2021.acl-short.35
[45]
Yuzhe Yang, Kaiwen Zha, Ying-Cong Chen, Hao Wang, and Dina Katabi. 2021. Delving into Deep Imbalanced Regression. CoRR abs/2102.09554(2021). arXiv:2102.09554https://arxiv.org/abs/2102.09554
[46]
Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Personalizing Dialogue Agents: I have a dog, do you have pets too?. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, Iryna Gurevych and Yusuke Miyao (Eds.). Association for Computational Linguistics, 2204–2213. https://doi.org/10.18653/v1/P18-1205
[47]
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. CoRR abs/1707.09457(2017). arXiv:1707.09457http://arxiv.org/abs/1707.09457
[48]
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers), Marilyn A. Walker, Heng Ji, and Amanda Stent (Eds.). Association for Computational Linguistics, 15–20. https://doi.org/10.18653/v1/n18-2003
[49]
Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. 2018. Learning Gender-Neutral Word Embeddings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii (Eds.). Association for Computational Linguistics, 4847–4853. https://aclanthology.org/D18-1521/
Index Terms
- E-VAN : Enhanced Variational AutoEncoder Network for Mitigating Gender Bias in Static Word Embeddings
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DOI:10.1145/3582768
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NLPIR 2022: 2022 6th International Conference on Natural Language Processing and Information Retrieval
December 16 - 18, 2022
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