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The advances in natural language processing (NLP) pose both opportunities and challenges.
A mathematical theory of communication
Claude Elwood Shannon · 1948
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter
Zeerak Waseem and Dirk Hovy · 2016
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi · 2017
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A challenge set approach to evaluating machine translation
Pierre Isabelle, Colin Cherry, and George Foster · 2017
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2017
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How grammatical is character-level neural machine translation? Assessing MT quality with contrastive translation pairs
Rico Sennrich · 2017
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Attention is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni · 2018
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Measuring and mitigating unintended bias in text classification
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2018
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Visualisation and ’diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure
Dieuwke Hupkes, Sara Veldhoen, and Willem Zuidema · 2018
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Examining gender and race bias in two hundred sentiment analysis systems
Svetlana Kiritchenko and Saif Mohammad · 2018
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Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig · 2018
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Reducing gender bias in abusive language detection
Ji Ho Park, Jamin Shin, and Pascale Fung · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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Analysis methods in neural language processing: A survey
Yonatan Belinkov and James Glass · 2019
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On the measure of intelligence
François Chollet · 2019
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner · 2019
Cited alongside, same era.
Counterfactual fairness in text classification through robustness
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H Chi, and Alex Beutel · 2019
Cited alongside, same era.
It’s all in the name: Mitigating gender bias with name-based counterfactual data substitution
Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel · 2019
Cited alongside, same era.
Intra-processing methods for debiasing neural networks
Yash Savani, Colin White, and Naveen Sundar Govindarajulu · 2020
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Optimized score transformation for fair classification
Dennis Wei, Karthikeyan Natesan Ramamurthy, and Flavio P Calmon · 2020
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Demographics should not be the reason of toxicity: Mitigating discrimination in text classifications with instance weighting
Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, and Tiejun Zhao · 2020
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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Attention head masking for inference time content selection in abstractive summarization
Shuyang Cao and Lu Wang · 2021
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Attention is not explanation
Sarthak Jain and Byron C Wallace · 2019
Cited alongside, same era.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
Interrogating the explanatory power of attention in neural machine translation
Pooya Moradi, Nishant Kambhatla, and Anoop Sarkar · 2019
Cited alongside, same era.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Is attention interpretable?
Sofia Serrano and Noah A Smith · 2019
Cited alongside, same era.
Mitigating gender bias in natural language processing: Literature review
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang · 2019
Cited alongside, same era.
BERT rediscovers the classical NLP pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick · 2019
Cited alongside, same era.
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Bold: Dataset and metrics for measuring biases in open-ended language generation
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta · 2021
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On-the-fly attention modulation for neural generation
Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, and Yejin Choi · 2021
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Improving bert with syntax-aware local attention
Zhongli Li, Qingyu Zhou, Chao Li, Ke Xu, and Yunbo Cao · 2021
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Attention calibration for transformer in neural machine translation
Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, and Mu Li · 2021
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StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy · 2021
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Do context-aware translation models pay the right attention?
Kayo Yin, Patrick Fernandes, Danish Pruthi, Aditi Chaudhary, André FT Martins, and Graham Neubig · 2021
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Entropy-based attention regularization frees unintended bias mitigation from lists
Giuseppe Attanasio, Debora Nozza, Dirk Hovy, and Elena Baralis · 2022
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Hibrids: Attention with hierarchical biases for structure-aware long document summarization
Shuyang Cao and Lu Wang · 2022
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Mabel: Attenuating gender bias using textual entailment data
Jacqueline He, Mengzhou Xia, Christiane Fellbaum, and Danqi Chen · 2022
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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BRIO: Bringing order to abstractive summarization
Yixin Liu, Pengfei Liu, Dragomir Radev, and Graham Neubig · 2022
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An empirical survey of the effectiveness of debiasing techniques for pre-trained language models
Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy · 2022
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Benchmarking bias mitigation algorithms in representation learning through fairness metrics
Charan Reddy · 2022
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Attention temperature matters in abstractive summarization distillation
Shengqiang Zhang, Xingxing Zhang, Hangbo Bao, and Furu Wei · 2022
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Deep learning on a healthy data diet: Finding important examples for fairness
Abdelrahman Zayed, Prasanna Parthasarathi, Goncalo Mordido, Hamid Palangi, Samira Shabanian, and Sarath Chandar · 2023
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