Elamur Research
☰
home / papers / 2022 / A Holistic Approach to Undesired Content Detection in the Real World

A Holistic Approach to Undesired Content Detection in the Real World

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2208.03274

Summary

Moderating the inputs and outputs of language models requires classifiers that cover many kinds of undesired content and that still work on real production traffic. In that traffic, harmful samples are rare and look different from academic benchmarks. The OpenAI authors present an end-to-end recipe rather than a single modelling trick. They define a taxonomy with five top-level categories (sexual, hateful, violent, self-harm and harassment content) plus severity subcategories. They also write precise labeling guidelines and refine them through regular calibration sessions with annotators. Training data is collected iteratively from production samples with PII masked, using an active learning loop that combines random, high-score and uncertainty-based selection. Robustness work includes synthetic and curated data to counter overfitting on surface templates, red-teaming, and hunting mislabeled items via cross-validation and token subtraction. Active learning finds far more rare harmful examples and strongly boosts rare-category performance, and the system beats off-the-shelf models. It powers the OpenAI Moderation endpoint and offers a reusable blueprint for other taxonomies.

Key points

  • The model outputs 8 labels, covering the five top-level categories (S, H, V, SH, HR) together with the three highest-severity subcategories (S3, H2, V2).
  • Undesired content is very rare in production. Self-harm appeared in about 0.04% of sampled prompts and threatening hate in about 0.017%.
  • Active learning yielded up to 22× more undesired samples and up to 10× better performance on rare categories. It used three pipelines: random, above-threshold and uncertainty sampling (scores near 0.5), with square-root reweighting by metadata.
  • Public or academic data helps for a noisy cold start but can hurt performance once enough properly labeled in-domain data exists.
  • Two resources are released: a labeled dataset (github.com/openai/moderation-api-release, built from CommonCrawl and model-generated text, with no production data) and the OpenAI Moderation endpoint.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Computing & GPUs

Cited by

125 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 115 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2208.03274 · arXiv 2208.03274

@misc{markov2022holistic,
  title = {{A Holistic Approach to Undesired Content Detection in the Real World}},
  author = {Markov, Todor and Zhang, Chong and Agarwal, Sandhini and Eloundou, Tyna and Lee, Teddy and Adler, Steven and Jiang, Angela and Weng, Lilian},
  year = {2022},
  doi = {10.48550/arxiv.2208.03274},
  eprint = {2208.03274},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2208.03274}
}

References

89 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Aluru, S. S.; Mathew, B.; Saha, P.; and Mukherjee, A. 2020. Deep learning models for multilingual hate speech detection. arXiv preprint arXiv:2004.06465. — [arXiv:2004.06465](https://arxiv.org/abs/2004.06465) find
  2. Alzantot, M.; Sharma, Y.; Elgohary, A.; Ho, B.-J.; Srivastava, M.; and Chang, K.-W. 2018. Generating Natural Language Adversarial Examples. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2890–2896. Brussels, Belgium: Association for Computational Linguisti… find our card
  3. Anaby-Tavor, A.; Carmeli, B.; Goldbraich, E.; Kantor, A.; Kour, G.; Shlomov, S.; Tepper, N.; and Zwerdling, N. 2020. Do not have enough data? Deep learning to the rescue! In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 7383–7390. find
  4. Arjovsky, M.; Chintala, S.; and Bottou, L. 2017. Wasserstein Generative Adversarial Networks. In Precup, D.; and Teh, Y. W., eds., Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, 214–223. find
  5. PMLR. Barbieri, F.; Camacho-Collados, J.; Espinosa Anke, L.; and Neves, L. 2020. TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification. In Findings of the Association for Computational Linguistics: EMNLP 2020, 1644–1650. Association for Computational Linguistics. Barriento… find our card
  6. Ben-David, S.; Blitzer, J.; Crammer, K.; Kulesza, A.; Pereira, F. C.; and Vaughan, J. W. 2009. A theory of learning from different domains. Machine Learning, 79: 151–175. find
  7. Ben-David, S.; Blitzer, J.; Crammer, K.; and Pereira, F. C. 2006. Analysis of Representations for Domain Adaptation. In NIPS. find
  8. Blitzer, J.; McDonald, R. T.; and Pereira, F. C. 2006. Domain Adaptation with Structural Correspondence Learning. In EMNLP. find
  9. Borkan, D.; Dixon, L.; Sorensen, J.; Thain, N.; and Vasserman, L. 2019. Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification. In Companion Proceedings of The 2019 World Wide Web Conference, WWW ’19, 491–500. New York, NY, USA: Association for Computing Machinery. ISBN… find
  10. Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33: 1877– 1901. Cohen, A. D.; Roberts, A.; Molina, A.; Butryna, A.; Jin… find
  11. Culotta, A.; and McCallum, A. 2005. Reducing labeling effort for structured prediction tasks. In AAAI, volume 5, 746– 751. find
  12. Dagan, I.; and Engelson, S. P. 1995. Committee-based sampling for training probabilistic classifiers. In Machine Learning Proceedings 1995, 150–157. Elsevier. Davani, A. M.; Dı́az, M.; and Prabhakaran, V. 2021. Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations. C… — [arXiv:2110.05719](https://arxiv.org/abs/2110.05719) find
  13. Davidson, T.; Bhattacharya, D.; and Weber, I. 2019. Racial Bias in Hate Speech and Abusive Language Detection Datasets. In Proceedings of the Third Workshop on Abusive Language Online, 25–35. Florence, Italy: Association for Computational Linguistics. find our card
  14. Davidson, T.; Warmsley, D.; Macy, M.; and Weber, I. 2017. Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media, volume 11, 512–515. de Gibert, O.; Perez, N.; Garcı́a-Pablos, A.; and Cuadros, M. 2018. Hate S… find our card
  15. Devlin, J.; Chang, M.; Lee, K.; and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Burstein, J.; Doran, C.; and Solorio, T., eds., Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguisti… find
  16. Dinan, E.; Humeau, S.; Chintagunta, B.; and Weston, J. 2019. Build it break it fix it for dialogue safety: Robustness from adversarial human attack. arXiv preprint arXiv:1908.06083. find our card
  17. Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012. Fairness through Awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS ’12, 214–226. New York, NY, USA: Association for Computing Machinery. ISBN 9781450311151. find
  18. Feng, S.; Wallace, E.; Grissom II, A.; Iyyer, M.; Rodriguez, P.; and Boyd-Graber, J. 2018. Pathologies of Neural Models Make Interpretations Difficult. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 3719–3728. Brussels, Belgium: Association for Computation… find
  19. Gal, Y.; Islam, R.; and Ghahramani, Z. 2017. Deep bayesian active learning with image data. In International Conference on Machine Learning, 1183–1192. find
  20. PMLR. Ganin, Y.; and Lempitsky, V. S. 2015. Unsupervised Domain Adaptation by Backpropagation. ArXiv, abs/1409.7495. — [arXiv:1409.7495](https://arxiv.org/abs/1409.7495) find
  21. Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016. Domain-Adversarial Training of Neural Networks. J. Mach. Learn. Res., 17(1): 2096–2030. find
  22. Gao, T.; Yao, X.; and Chen, D. 2021. SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Empirical Methods in Natural Language Processing (EMNLP). find our card
  23. Garg, S.; Perot, V.; Limtiaco, N.; Taly, A.; Chi, E. H.; and Beutel, A. 2019. Counterfactual Fairness in Text Classification through Robustness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’19, 219–226. New York, NY, USA: Association for Computing Machinery. ISBN… find
  24. Gehman, S.; Gururangan, S.; Sap, M.; Choi, Y.; and Smith, N. A. 2020. Realtoxicityprompts: Evaluating neural toxic degeneration in language models. arXiv preprint arXiv:2009.11462. find our card
  25. Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A. C.; and Bengio, Y. 2014. find
  26. Generative Adversarial Nets. In NIPS. Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015. Explaining and Harnessing Adversarial Examples. CoRR, abs/1412.6572. find our card
  27. Guo, H.; Mao, Y.; and Zhang, R. 2019. Augmenting data with mixup for sentence classification: An empirical study. arXiv preprint arXiv:1905.08941. — [arXiv:1905.08941](https://arxiv.org/abs/1905.08941) find
  28. Hartvigsen, T.; Gabriel, S.; Palangi, H.; Sap, M.; Ray, D.; and Kamar, E. 2022. Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection. arXiv preprint arXiv:2203.09509. find our card
  29. Hoi, S. C.; Jin, R.; Zhu, J.; and Lyu, M. R. 2006. Batch mode active learning and its application to medical image classification. In Proceedings of the 23rd international conference on Machine learning, 417–424. Jigsaw. ???? Perspective API. https://www.perspectiveapi. com/. Accessed: 2022-06-15. ↗
  30. Jigsaw. 2018. Toxic Comment Classification Challenge. https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/overview. Accessed: 2022-06-15. ↗
  31. Kiela, D.; Bartolo, M.; Nie, Y.; Kaushik, D.; Geiger, A.; Wu, Z.; Vidgen, B.; Prasad, G.; Singh, A.; Ringshia, P.; Ma, Z.; Thrush, T.; Riedel, S.; Waseem, Z.; Stenetorp, P.; Jia, R.; Bansal, M.; Potts, C.; and Williams, A. 2021. Dynabench: Rethinking Benchmarking in NLP. In Proceedings of the 2021 C… find
  32. Kobayashi, S. 2018. Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), 452–457. New Orleans, Louis… find our card
  33. Kumar, V.; Choudhary, A.; and Cho, E. 2020. Data augmentation using pre-trained transformer models. arXiv preprint arXiv:2003.02245. find our card
  34. Kusner, M. J.; Loftus, J.; Russell, C.; and Silva, R. 2017. Counterfactual Fairness. In Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H.; Fergus, R.; Vishwanathan, S.; and Garnett, R., eds., Advances in Neural Information Processing Systems 30, 4066–4076. find
  35. Curran Associates, Inc. Kwok, I.; and Wang, Y. 2013. Locate the Hate: Detecting Tweets against Blacks. In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, AAAI’13, 1621–1622. find
  36. AAAI Press. Lees, A.; Tran, V. Q.; Tay, Y.; Sorensen, J.; Gupta, J.; Metzler, D.; and Vasserman, L. 2022. A new generation of perspective api: Efficient multilingual character-level transformers. arXiv preprint arXiv:2202.11176. find
  37. Lewis, D. D.; and Catlett, J. 1994. Heterogeneous uncertainty sampling for supervised learning. In Machine learning proceedings 1994, 148–156. find
  38. Elsevier. Lewis, D. D.; and Gale, W. A. 1994. A sequential algorithm for training text classifiers. In SIGIR’94, 3–12. find
  39. Springer. Luo, T.; Kramer, K.; Goldgof, D. B.; Hall, L. O.; Samson, S.; Remsen, A.; Hopkins, T.; and Cohn, D. 2005. Active learning to recognize multiple types of plankton. Journal of Machine Learning Research, 6(4). find
  40. Mansour, Y.; Mohri, M.; and Rostamizadeh, A. 2008. Domain Adaptation with Multiple Sources. In NIPS. find
  41. McCallum, A.; and Nigam, K. 1998. Employing EM and Pool-Based Active Learning for Text Classification. In Proceedings of the Fifteenth International Conference on Machine Learning, ICML ’98, 350–358. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. ISBN 1558605568. find
  42. Mishkin, P.; Ahmad, L.; Brundage, M.; Krueger, G.; and Sastry, G. 2022. DALL·E 2 Preview - Risks and Limitations. find
  43. Mollas, I.; Chrysopoulou, Z.; Karlos, S.; and Tsoumakas, G. 2020. ETHOS: an online hate speech detection dataset. arXiv preprint arXiv:2006.08328. — [arXiv:2006.08328](https://arxiv.org/abs/2006.08328) find
  44. Nguyen, H. T.; and Smeulders, A. 2004. Active learning using pre-clustering. In Proceedings of the twenty-first international conference on Machine learning. find
  45. Nobata, C.; Tetreault, J.; Thomas, A.; Mehdad, Y.; and Chang, Y. 2016. Abusive Language Detection in Online User Content. In Proceedings of the 25th International Conference on World Wide Web, WWW ’16, 145–153. Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Com… find
  46. Ovesdotter Alm, C. 2011. Subjective Natural Language Problems: Motivations, Applications, Characterizations, and Implications. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 107–112. Portland, Oregon, USA: Association for Comp… find
  47. PAI. 2021. https://partnershiponai.org/paper/responsible-sourcing-considerations/. ↗
  48. Patton, D.; Blandfort, P.; Frey, W.; Gaskell, M.; and Karaman, S. 2018. Annotating Twitter Data from Vulnerable Populations: Evaluating Disagreement Between Domain Experts and Graduate Student Annotators. find
  49. Pavlopoulos, J.; Sorensen, J.; Dixon, L.; Thain, N.; and Androutsopoulos, I. 2020. Toxicity detection: Does context really matter? arXiv preprint arXiv:2006.00998. find
  50. Perez, E.; Huang, S.; Song, F.; Cai, T.; Ring, R.; Aslanides, J.; Glaese, A.; McAleese, N.; and Irving, G. 2022. Red teaming language models with language models. arXiv preprint arXiv:2202.03286. find our card
  51. Prabhakaran, V.; Mostafazadeh Davani, A.; and Diaz, M. 2021. On Releasing Annotator-Level Labels and Information in Datasets. In Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop. Punta Cana, Dominican Republic: Association fo… find
  52. Ramponi, A.; and Plank, B. 2020. Neural Unsupervised Domain Adaptation in NLP—A Survey. ArXiv, abs/2006.00632. find
  53. Reddit. 2022. Building Better Moderator Tools. Accessed: 2022-08-04. find
  54. Ribeiro, M. T.; Wu, T.; Guestrin, C.; and Singh, S. 2020. Beyond Accuracy: Behavioral Testing of NLP Models with CheckList. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4902–4912. Online: Association for Computational Linguistics. find
  55. Rosenthal, S.; Atanasova, P.; Karadzhov, G.; Zampieri, M.; and Nakov, P. 2020. A Large-Scale Semi-Supervised Dataset for Offensive Language Identification. arXiv preprint arXiv:2004.14454. Röttger, P.; Vidgen, B.; Nguyen, D.; Waseem, Z.; Margetts, H.; and Pierrehumbert, J. 2021. HateCheck: Function… find our card
  56. Sap, M.; Card, D.; Gabriel, S.; Choi, Y.; and Smith, N. A. 2019. The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1668–1678. Florence, Italy: Association for Computational Linguistics. find our card
  57. Scheffer, T.; Decomain, C.; and Wrobel, S. 2001. Active hidden markov models for information extraction. In International Symposium on Intelligent Data Analysis, 309–318. Springer. Schick, T.; and Schütze, H. 2021. Generating datasets with pretrained language models. arXiv preprint arXiv:2104.07540… find our card
  58. Schmidt, S.; Rao, Q.; Tatsch, J.; and Knoll, A. 2020. Advanced active learning strategies for object detection. In 2020 IEEE Intelligent Vehicles Symposium (IV), 871–876. find
  59. IEEE. Schohn, G.; and Cohn, D. 2000. Less is more: Active learning with support vector machines. In ICML. find
  60. Settles, B.; and Craven, M. 2008. An analysis of active learning strategies for sequence labeling tasks. In proceedings of the 2008 conference on empirical methods in natural language processing, 1070–1079. find
  61. Seung, H. S.; Opper, M.; and Sompolinsky, H. 1992. Query by committee. In Proceedings of the fifth annual workshop on Computational learning theory, 287–294. find
  62. Shah, D.; Lei, T.; Moschitti, A.; Romeo, S.; and Nakov, P. 2018. Adversarial Domain Adaptation for Duplicate Question Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 1056– 1063. Brussels, Belgium: Association for Computational Linguistics. find
  63. Shen, D.; Zheng, M.; Shen, Y.; Qu, Y.; and Chen, W. 2020. A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation. arXiv preprint arXiv:2009.13818. — [arXiv:2009.13818](https://arxiv.org/abs/2009.13818) find
  64. Shen, J.; Qu, Y.; Zhang, W.; and Yu, Y. 2018. Wasserstein Distance Guided Representation Learning for Domain Adaptation. In AAAI. find
  65. Shen, X.; and Zhai, C. 2005. Active feedback in ad hoc information retrieval. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 59–66. find
  66. Siddhant, A.; and Lipton, Z. C. 2018. Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2904–2909. Brussels, Belgium: Association for Computational Lingui… find
  67. Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I. J.; and Fergus, R. 2013. Intriguing properties of neural networks. CoRR, abs/1312.6199. find our card
  68. Tzeng, E.; Hoffman, J.; Saenko, K.; and Darrell, T. 2017. find
  69. Adversarial Discriminative Domain Adaptation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2962–2971. find
  70. Vidgen, B.; and Derczynski, L. 2020. Directions in abusive language training data, a systematic review: Garbage in, garbage out. Plos one, 15(12): e0243300. find
  71. Vidgen, B.; Harris, A.; Nguyen, D.; Tromble, R.; Hale, S.; and Margetts, H. 2019. Challenges and frontiers in abusive content detection. In Proceedings of the Third Workshop on Abusive Language Online, 80–93. Florence, Italy: Association for Computational Linguistics. find
  72. Vidgen, B.; Thrush, T.; Waseem, Z.; and Kiela, D. 2020. Learning from the worst: Dynamically generated datasets to improve online hate detection. arXiv preprint arXiv:2012.15761. find our card
  73. Wang, C.; and Banko, M. 2021. Practical Transformer-based Multilingual Text Classification. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, 121–129. Online: Association for Computation… find
  74. Wang, S.; Liu, Y.; Xu, Y.; Zhu, C.; and Zeng, M. 2021a. Want To Reduce Labeling Cost? GPT-3 Can Help. In Findings of the Association for Computational Linguistics: EMNLP 2021, 4195–4205. Punta Cana, Dominican Republic: Association for Computational Linguistics. find our card
  75. Wang, Z.; Yu, A. W.; Firat, O.; and Cao, Y. 2021b. Towards zero-label language learning. arXiv preprint arXiv:2109.09193. — [arXiv:2109.09193](https://arxiv.org/abs/2109.09193) find
  76. Waseem, Z. 2016. Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter. In Proceedings of the First Workshop on NLP and Computational Social Science, 138–142. Austin, Texas: Association for Computational Linguistics. find
  77. Wei, J.; and Zou, K. 2019. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP),… find our card
  78. Weidinger, L.; Mellor, J.; Rauh, M.; Griffin, C.; Uesato, J.; Huang, P.-S.; Cheng, M.; Glaese, M.; Balle, B.; Kasirzadeh, A.; et al. 2021. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359. find our card
  79. Weiss, K. R.; Khoshgoftaar, T. M.; and Wang, D. 2016. A survey of transfer learning. Journal of Big Data, 3: 1–40. find
  80. Welbl, J.; Glaese, A.; Uesato, J.; Dathathri, S.; Mellor, J.; Hendricks, L. A.; Anderson, K.; Kohli, P.; Coppin, B.; and Huang, P.-S. 2021. Challenges in detoxifying language models. arXiv preprint arXiv:2109.07445. find
  81. Xu, Z.; Akella, R.; and Zhang, Y. 2007. Incorporating diversity and density in active learning for relevance feedback. In European Conference on Information Retrieval, 246–257. find
  82. Springer. Yin, W.; and Zubiaga, A. 2021. Towards generalisable hate speech detection: a review on obstacles and solutions. PeerJ Computer Science, 7: e598. find
  83. Yoo, K. M.; Park, D.; Kang, J.; Lee, S.-W.; and Park, W. 2021. GPT3Mix: Leveraging large-scale language models for text augmentation. arXiv preprint arXiv:2104.08826. find our card
  84. YouTube. 2019. The Four Rs of Responsibility, Part 1: Removing Harmful Content. Accessed: 2022-08-04. find
  85. Zampieri, M.; Malmasi, S.; Nakov, P.; Rosenthal, S.; Farra, N.; and Kumar, R. 2019. Predicting the Type and Target of Offensive Posts in Social Media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vo… find our card
  86. Zeng, X.; Garg, S.; Chatterjee, R.; Nallasamy, U.; and Paulik, M. 2019. Empirical evaluation of active learning techniques for neural MT. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), 84–93. find
  87. Zhang, C.; Zhao, J.; Zhang, H.; Chang, K.-W.; and Hsieh, C.-J. 2021. Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolog… find
  88. Zhao, J.; Wang, T.; Yatskar, M.; Ordonez, V.; and Chang, K.-W. 2017. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2979–2989. Copenhagen, Denmark: Association for C… find our card
  89. Ziegler, D. M.; Nix, S.; Chan, L.; Bauman, T.; Schmidt-Nielsen, P.; Lin, T.; Scherlis, A.; Nabeshima, N.; Weinstein-Raun, B.; de Haas, D.; et al. 2022. Adversarial Training for High-Stakes Reliability. arXiv preprint arXiv:2205.01663. — [arXiv:2205.01663](https://arxiv.org/abs/2205.01663) find