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Neural networks for NLP are becoming increasingly complex and widespread, and there is a growing concern if these models are responsible to use.
LIII. On lines and planes of closest fit to systems of points in space
Karl Pearson. 1901 · 1901
Earlier work this paper cites.
Are Sixteen Heads Really Better than One?
Paul Michel, Omer Levy, and Graham Neubig. 2019 · 1905
Earlier work this paper cites.
Did the model understand the question?. In ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) , Vol. 1. 1896–1906
Pramod K. Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere. 2018 · 1906
Earlier work this paper cites.
Explaining classifiers with causal concept effect (CaCE)
Yash Goyal, Uri Shalit, and Been Kim. 2019 · 1907
Earlier work this paper cites.
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. 2019a · 1907
Earlier work this paper cites.
Attention Interpretability Across NLP Tasks
Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, and Manaal Faruqui. 2019 · 1909
Earlier work this paper cites.
A value for N-Person Games
Shapley. 1953 · 1953
Earlier work this paper cites.
A general rotation criterion and its use in orthogonal rotation
Charles B. Crawford and George A. Ferguson. 1970 · 1970
Earlier work this paper cites.
Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression
R. Dennis Cook and Sanford Weisberg. 1980 · 1980
Earlier work this paper cites.
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
Earlier work this paper cites.
BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL ’02 , Vol. 371. Association for Computational Linguistics, Morristown, NJ, USA, 311
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2001 · 2001
Earlier work this paper cites.
Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI’01) . Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 411–420
Judea Pearl. 2001 · 2001
Earlier work this paper cites.
A Generalized Representer Theorem
Bernhard Schölkopf, Ralf Herbrich, and Alex J. Smola. 2001 · 2001
Earlier work this paper cites.
Garima Pruthi, Frederick Liu, Mukund Sundararajan, and Satyen Kale. 2020 · 2002
Earlier work this paper cites.
Latent Dirichlet allocation
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003 · 2003
Earlier work this paper cites.
Model Agnostic Multilevel Explanations
Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, and Amit Dhurandhar. 2020 · 2003
Earlier work this paper cites.
Explaining Question Answering Models through Text Generation
Veronica Latcinnik and Jonathan Berant. 2020 · 2004
Earlier work this paper cites.
Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis
Anna B. Costello and Jason W. Osborne. 2005 · 2005
Earlier work this paper cites.
HotFlip: White-Box Adversarial Examples for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics , Vol. 2. Association for Computational Linguistics, Stroudsburg, PA, USA, 31–36
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2006
Earlier work this paper cites.
Compositional Explanations of Neurons. In Advances in Neural Information Processing Systems
Jesse Mu and Jacob Andreas. 2020 · 2006
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van Der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Reading Tea Leaves: How Humans Interpret Topic Models. In Advances in Neural Information Processing Systems , Y Bengio, D Schuurmans, J Lafferty, C Williams, and A Culotta (Eds.), Vol. 22. Curran Associates, Inc., 288–296
Jonathan Chang, Jordan Boyd-graber, Sean Gerrish, Chong Wang, David M. Blei, Jordan Boyd-graber, and David M. Blei. 2009 · 2009
Earlier work this paper cites.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus Robert Müller. 2010 · 2010
Earlier work this paper cites.
Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
Daniel E Ho and Alice Xiang. 2020 · 2012
Earlier work this paper cites.
Information complexity in bandit subset selection. In Journal of Machine Learning Research
Emilie Kaufmann and Shivaram Kalyanakrishnan. 2013 · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Parsing with compositional vector grammars
Richard Socher, John Bauer, Christopher D. Manning, and Andrew Y. Ng. 2013a · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013b · 2013
Earlier work this paper cites.
Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Stroudsburg, PA, USA, 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Neural Module Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 39–48
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Advances in Neural Information Processing Systems . 4356–4364
Tolga Bolukbasi, Kai Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016 · 2016
Earlier work this paper cites.
Rationalizing Neural Predictions. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Stroudsburg, PA, USA, 107–117
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Earlier work this paper cites.
Visualizing and Understanding Neural Models in NLP. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, Stroudsburg, PA, USA, 681–691
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016 · 2016
Earlier work this paper cites.
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
Earlier work this paper cites.
"Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , Vol. 13-17-Augu. ACM, New York, NY, USA, 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Does string-based neural MT learn source syntax?. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Stroudsburg, PA, USA, 1526–1534
Xing Shi, Inkit Padhi, and Kevin Knight. 2016 · 2016
Earlier work this paper cites.
AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale . ACM, New York, NY, USA, 379–388
Joseph Jay Williams, Juho Kim, Anna Rafferty, Samuel Maldonado, Krzysztof Z. Gajos, Walter S. Lasecki, and Neil Heffernan. 2016 · 2016
Earlier work this paper cites.
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2017 · 2017
Earlier work this paper cites.
A causal framework for explaining the predictions of black-box sequence-to-sequence models. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Stroudsburg, PA, USA, 412–421
David Alvarez-Melis and Tommi Jaakkola. 2017 · 2017
Earlier work this paper cites.
Translating neuralese
Jacob Andreas, Anca Dragan, and Dan Klein. 2017 · 2017
Earlier work this paper cites.
Gino Brunner, Yuyi Wang, Roger Wattenhofer, and Michael Weigelt. 2017 · 2017
Earlier work this paper cites.
Interpretability of deep learning models: A survey of results. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) . IEEE, 1–6
Supriyo Chakraborty, Richard Tomsett, Ramya Raghavendra, Daniel Harborne, Moustafa Alzantot, Federico Cerutti, Mani Srivastava, Alun Preece, Simon Julier, Raghuveer M. Rao, Troy D. Kelley, Dave Braines, Murat Sensoy, Christopher J. Willis, and Prudhvi Gurram. 2017 · 2017
Earlier work this paper cites.
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Earlier work this paper cites.
Accountability of AI Under the Law: The Role of Explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Christopher Bavitz, Samuel J. Gershman, David O’Brien, Stuart Shieber, Jim Waldo, David Weinberger, and Alexandra Wood. 2017 · 2017
Earlier work this paper cites.
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh and Percy Liang. 2017 · 2017
Cited alongside, same era.
Scott Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Rotated Word Vector Representations and their Interpretability. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Stroudsburg, PA, USA, 401–411
Sungjoon Park, JinYeong Bak, and Alice Oh. 2017 · 2017
Cited alongside, same era.
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Cited alongside, same era.
On the (In)fidelity and Sensitivity of Explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, Pradeep K Ravikumar, Arun Sai Suggala, David I Inouye, and Pradeep K Ravikumar. 2019 · 2019
Later among the works it cites.
PAWS: Paraphrase adversaries from word scrambling. In Proceedings of the 2019 Conference of the North . Association for Computational Linguistics, Stroudsburg, PA, USA, 1298–1308
Yuan Zhang, Jason Baldridge, and Luheng He. 2019 · 2019
Later among the works it cites.
Quantifying Attention Flow in Transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Stroudsburg, PA, USA, 4190–4197
Samira Abnar and Willem Zuidema. 2020 · 2020
Later among the works it cites.
SAM: The Sensitivity of Attribution Methods to Hyperparameters. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, 11–21
Naman Bansal, Chirag Agarwal, and Anh Nguyen. 2020 · 2020
Later among the works it cites.
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi and Mohammed Berrada. 2018 · 2018
Cited alongside, same era.
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018 · 2018
Cited alongside, same era.
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Cited alongside, same era.
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Stroudsburg, PA, USA, 2126–2136
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2018 · 2018
Cited alongside, same era.
The mythos of model interpretability
Zachary C Lipton. 2018 · 2018
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2018
Cited alongside, same era.
Improving Language Understanding by Generative Pre-Training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Cited alongside, same era.
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP . Association for Computational Linguistics, Stroudsburg, PA, USA, 149–155
Jasmijn Bastings and Katja Filippova. 2020 · 2020
Later among the works it cites.
Interpretability and Analysis in Neural NLP. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts . Association for Computational Linguistics, Stroudsburg, PA, USA, 1–5
Yonatan Belinkov, Sebastian Gehrmann, and Ellie Pavlick. 2020 · 2020
Later among the works it cites.
Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems , H Larochelle, M Ranzato, R Hadsell, M F Balcan, and H Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877–1901
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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
Later among the works it cites.
On Identifiability in Transformers. In International Conference on Learning Representations (ICLR 2020)
Gino Brunner, Yang Liu, Damián Pascual, Oliver Richter, Massimiliano Ciaramita, and Roger Wattenhofer. 2020 · 2020
Later among the works it cites.
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
A. Chatzimparmpas, R. M. Martins, I. Jusufi, K. Kucher, F. Rossi, and A. Kerren. 2020 · 2020
Later among the works it cites.
A Survey of the State of Explainable AI for Natural Language Processing. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing . Association for Computational Linguistics, Suzhou, China, 447–459
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen. 2020 · 2020
Later among the works it cites.
Evaluating Models’ Local Decision Boundaries via Contrast Sets. In Findings of the Association for Computational Linguistics: EMNLP 2020 . Association for Computational Linguistics, Stroudsburg, PA, USA, 1307–1323
Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020
Later among the works it cites.
Neural Module Networks for Reasoning over Text. In International Conference on Learning Representations (ICLR)
Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, and Matt Gardner. 2020 · 2020
Later among the works it cites.
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Stroudsburg, PA, USA, 5553–5563
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. 2020 · 2020
Later among the works it cites.
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?. In Findings of the Association for Computational Linguistics: EMNLP 2020 . Association for Computational Linguistics, Stroudsburg, PA, USA, 4351–4367
Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal. 2020 · 2020
Later among the works it cites.
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Stroudsburg, PA, USA, 4198–4205
Alon Jacovi and Yoav Goldberg. 2020 · 2020
Later among the works it cites.
Learning The Difference That Makes A Difference With Counterfactually-Augmented Data. In International Conference on Learning Representations
Divyansh Kaushik, Eduard Hovy, and Zachary C. Lipton. 2020 · 2020
Later among the works it cites.
NILE : Natural Language Inference with Faithful Natural Language Explanations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Stroudsburg, PA, USA, 8730–8742
Sawan Kumar and Partha Talukdar. 2020 · 2020
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Later among the works it cites.
A Primer in BERTology: What We Know About How BERT Works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2020
Later among the works it cites.
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Guided-LIME: Structured sampling based hybrid approach towards explaining blackbox machine learning models. In CEUR Workshop Proceedings , Vol. 2699
Amit Sangroya, Mouli Rastogi, C Anantaram, and Lovekesh Vig. 2020 · 2020
Later among the works it cites.
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society . ACM, New York, NY, USA, 180–186
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. 2020 · 2020
Later among the works it cites.
Obtaining Faithful Interpretations from Compositional Neural Networks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Stroudsburg, PA, USA, 5594–5608
Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, and Matt Gardner. 2020 · 2020
Later among the works it cites.
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Association for Computational Linguistics, Stroudsburg, PA, USA, 107–118
Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020 · 2020
Later among the works it cites.
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Erico Tjoa and Cuntai Guan. 2020 · 2020
Later among the works it cites.
Investigating Gender Bias in Language Models Using Causal Mediation Analysis. In Advances in Neural Information Processing Systems , H Larochelle, M Ranzato, R Hadsell, M F Balcan, and H Lin (Eds.), Vol. 33. Curran Associates, Inc., 12388–12401
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber. 2020 · 2020
Later among the works it cites.
Information-Theoretic Probing with Minimum Description Length. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Stroudsburg, PA, USA, 183–196
Elena Voita and Ivan Titov. 2020 · 2020
Later among the works it cites.
Interpreting Predictions of NLP Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts . Association for Computational Linguistics, Stroudsburg, PA, USA, 20–23
Eric Wallace, Matt Gardner, and Sameer Singh. 2020 · 2020
Later among the works it cites.
Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Association for Computational Linguistics, Stroudsburg, PA, USA, 38–45
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Later among the works it cites.
Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, and Katja Filippova. 2021 · 2021
Closest in time.
Probing Classifiers: Promises, Shortcomings, and Advances
Yonatan Belinkov. 2021 · 2021
Closest in time.
On the Dangers of Stochastic Parrots. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency . ACM, New York, NY, USA, 610–623
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Closest in time.
A Survey on Bias in Deep NLP
Ismael Garrido-Muñoz, Arturo Montejo-Ráez, Fernando Martínez-Santiago, and L. Alfonso Ureña-López. 2021 · 2021
Closest in time.
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Stroudsburg, PA, USA, 10333–10350
Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, and Caiming Xiong. 2021 · 2021
Closest in time.
Are Training Resources Insufficient? Predict First Then Explain!
Myeongjun Jang and Thomas Lukasiewicz. 2021 · 2021
Closest in time.
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021 · 2021
Closest in time.
Explaining NLP Models via Minimal Contrastive Editing (MiCE). In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . Association for Computational Linguistics, Stroudsburg, PA, USA, 3840–3852
Alexis Ross, Ana Marasović, and Matthew Peters. 2021 · 2021
Closest in time.
Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, and Adina Williams. 2021 · 2021
Closest in time.
Interpreting Deep Learning Models in Natural Language Processing: A Review
Xiaofei Sun, Diyi Yang, Xiaoya Li, Tianwei Zhang, Yuxian Meng, Han Qiu, Guoyin Wang, Eduard Hovy, and Jiwei Li. 2021 · 2021
Closest in time.
Towards a Robust Deep Neural Network against Adversarial Texts: A Survey
Wenqi Wang, Run Wang, Lina Wang, Zhibo Wang, and Aoshuang Ye. 2021 · 2021
Closest in time.
Sarah Wiegreffe and Ana Marasović. 2021 · 2021
Closest in time.
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . Association for Computational Linguistics, Stroudsburg, PA, USA, 6707–6723
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel Weld. 2021 · 2021
Closest in time.
Local Structure Matters Most: Perturbation Study in NLU. In Findings of the Association for Computational Linguistics: ACL 2022 . Association for Computational Linguistics, Stroudsburg, PA, USA, 3712–3731
Louis Clouatre, Prasanna Parthasarathi, Amal Zouaq, and Sarath Chandar. 2022 · 2022
Closest in time.
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining. In Findings of the Association for Computational Linguistics: EMNLP 2022 . Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 1731–1751
Andreas Madsen, Nicholas Meade, Vaibhav Adlakha, and Siva Reddy. 2022 · 2022
Closest in time.
What’s in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Stroudsburg, PA, USA, 2067–2073
Arne Köhn. 2015 · 2073
Closest in time.