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Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity concerns.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Causal Diagrams for Empirical Research
Pearl, J. 1995 · 1995
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Bias in computer systems
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Long Short-Term Memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
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Support vector machines
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Multi-dimensional gender bias classification
Dinan, E.; Fan, A.; Wu, L.; Weston, J.; Kiela, D.; and Williams, A. 2020 · 2005
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Empirical evaluation of gated recurrent neural networks on sequence modeling
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016 · 2016
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Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Waseem, Z.; and Hovy, D. 2016 · 2016
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Adi, Y.; Kermany, E.; Belinkov, Y.; Lavi, O.; and Goldberg, Y. 2017 · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Beutel, A.; Chen, J.; Zhao, Z.; and Chi, E. H. 2017 · 2017
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A Challenge Set Approach to Evaluating Machine Translation
Isabelle, P.; Cherry, C.; and Foster, G. 2017 · 2017
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How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs
Sennrich, R. 2017 · 2017
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Attention is All You Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Budzianowski, P.; Wen, T.-H.; Tseng, B.-H.; Casanueva, I.; Ultes, S.; Ramadan, O.; and Gašić, M. 2018 · 2018
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Conneau, A.; Kruszewski, G.; Lample, G.; Barrault, L.; and Baroni, M. 2018 · 2018
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Measuring and mitigating unintended bias in text classification
Dixon, L.; Li, J.; Sorensen, J.; Thain, N.; and Vasserman, L. 2018 · 2018
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Visualisation and ’diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure
Hupkes, D.; Veldhoen, S.; and Zuidema, W. 2018 · 2018
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Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Kiritchenko, S.; and Mohammad, S. 2018 · 2018
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Learning adversarially fair and transferable representations
Madras, D.; Creager, E.; Pitassi, T.; and Zemel, R. 2018 · 2018
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Stress Test Evaluation for Natural Language Inference
Naik, A.; Ravichander, A.; Sadeh, N.; Rose, C.; and Neubig, G. 2018 · 2018
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Reducing Gender Bias in Abusive Language Detection
Park, J. H.; Shin, J.; and Fung, P. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; and Sutskever, I. 2018 · 2018
Learning controllable fair representations
Song, J.; Kalluri, P.; Grover, A.; Zhao, S.; and Ermon, S. 2019 · 2019
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BERT Rediscovers the Classical NLP Pipeline
Tenney, I.; Das, D.; and Pavlick, E. 2019 · 2019
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An Empirical Study of Example Forgetting during Deep Neural Network Learning
Toneva, M.; Sordoni, A.; des Combes, R. T.; Trischler, A.; Bengio, Y.; and Gordon, G. J. 2019 · 2019
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SuperGLUE: A stickier benchmark for general-purpose language understanding systems
Wang, A.; Pruksachatkun, Y.; Nangia, N.; Singh, A.; Michael, J.; Hill, F.; Levy, O.; and Bowman, S. 2019 · 2019
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XLNet: Generalized autoregressive pretraining for language understanding
Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R. R.; and Le, Q. V. 2019 · 2019
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Language models are few-shot learners
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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A.; Singh, A.; Michael, J.; Hill, F.; Levy, O.; and Bowman, S. 2018 · 2018
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Personalizing Dialogue Agents: I have a dog, do you have pets too?
Zhang, S.; Dinan, E.; Urbanek, J.; Szlam, A.; Kiela, D.; and Weston, J. 2018 · 2018
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Analysis methods in neural language processing: A survey
Belinkov, Y.; and Glass, J. 2019 · 2019
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Understanding the origins of bias in word embeddings
Brunet, M.-E.; Alkalay-Houlihan, C.; Anderson, A.; and Zemel, R. 2019 · 2019
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
De-Arteaga, M.; Romanov, A.; Wallach, H.; Chayes, J.; Borgs, C.; Chouldechova, A.; Geyik, S.; Kenthapadi, K.; and Kalai, A. T. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2020
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Invariant representations through adversarial forgetting
Jaiswal, A.; Moyer, D.; Ver Steeg, G.; AbdAlmageed, W.; and Natarajan, P. 2020 · 2020
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Gender bias in neural natural language processing
Lu, K.; Mardziel, P.; Wu, F.; Amancharla, P.; and Datta, A. 2020 · 2020
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Mark-Evaluate: Assessing Language Generation using Population Estimation Methods
Mordido, G.; and Meinel, C. 2020 · 2020
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
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Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation
Sauder, J.; Hu, T.; Che, X.; Mordido, G.; Yang, H.; and Meinel, C. 2020 · 2020
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Investigating Gender Bias in Language Models Using Causal Mediation Analysis
Vig, J.; Gehrmann, S.; Belinkov, Y.; Qian, S.; Nevo, D.; Singer, Y.; and Shieber, S. 2020 · 2020
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Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting
Zhang, G.; Bai, B.; Zhang, J.; Bai, K.; Zhu, C.; and Zhao, T. 2020 · 2020
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Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Gupta, U.; Ferber, A.; Dilkina, B.; and Ver Steeg, G. 2021 · 2021
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StereoSet: Measuring stereotypical bias in pretrained language models
Nadeem, M.; Bethke, A.; and Reddy, S. 2021 · 2021
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Deep Learning on a Data Diet: Finding Important Examples Early in Training
Paul, M.; Ganguli, S.; and Dziugaite, G. K. 2021 · 2021
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An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Meade, N.; Poole-Dayan, E.; and Reddy, S. 2022 · 2022
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