Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Advances in Neural Information Processing Systems, pp. 4349–4357 (2016)
Google Scholar
Caliskan, A., Bryson, J.J., Narayanan, A.: Semantics derived automatically from language corpora contain human-like biases. Science 356(6334), 183–186 (2017)
Article
Google Scholar
Clark, K., Manning, C.D.: Deep reinforcement learning for mention-ranking coreference models. arXiv preprint arXiv:1609.08667 (2016)
Clark, K., Manning, C.D.: Improving coreference resolution by learning entity-level distributed representations. arXiv preprint arXiv:1606.01323 (2016)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Font, J.E., Costa-Jussa, M.R.: Equalizing gender biases in neural machine translation with word embeddings techniques. arXiv preprint arXiv:1901.03116 (2019)
Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)
Google Scholar
Johnson, M., et al.: Google’s multilingual neural machine translation system: enabling zero-shot translation. TACL 5, 339–351 (2017). https://transacl.org/ojs/index.php/tacl/article/view/1081
Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., Wu, Y.: Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410 (2016)
Kaushik, D., Hovy, E., Lipton, Z.C.: Learning the difference that makes a difference with counterfactually-augmented data. arXiv preprint arXiv:1909.12434 (2019)
Lapowsky, I.: Google autocomplete still has a hitler problem, February 2018. https://www.wired.com/story/google-autocomplete-vile-suggestions/
Lee, K., He, L., Lewis, M., Zettlemoyer, L.: End-to-end neural coreference resolution. arXiv preprint arXiv:1707.07045 (2017)
Lu, K., Mardziel, P., Wu, F., Amancharla, P., Datta, A.: Gender bias in neural natural language processing. arXiv preprint arXiv:1807.11714 (2018)
Manzini, T., Lim, Y.C., Tsvetkov, Y., Black, A.W.: Black is to criminal as caucasian is to police: detecting and removing multiclass bias in word embeddings. arXiv preprint arXiv:1904.04047 (2019)
May, C., Wang, A., Bordia, S., Bowman, S.R., Rudinger, R.: On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561 (2019)
Merity, S., Xiong, C., Bradbury, J., Socher, R.: Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843 (2016)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Pradhan, S., Moschitti, A., Xue, N., Uryupina, O., Zhang, Y.: CoNLL-2012 shared task: modeling multilingual unrestricted coreference in ontonotes. In: Joint Conference on EMNLP and CoNLL-Shared Task, pp. 1–40. Association for Computational Linguistics (2012)
Google Scholar
Rudinger, R., Naradowsky, J., Leonard, B., Van Durme, B.: Gender bias in coreference resolution. arXiv preprint arXiv:1804.09301 (2018)
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)
Google Scholar
Tatman, R.: Gender and dialect bias in YouTube’s automatic captions. In: Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, pp. 53–59 (2017)
Google Scholar
Vanmassenhove, E., Hardmeier, C., Way, A.: Getting gender right in neural machine translation. arXiv preprint arXiv:1909.05088 (2019)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)
Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordonez, V., Chang, K.W.: Gender bias in contextualized word embeddings. arXiv preprint arXiv:1904.03310 (2019)
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Gender bias in coreference resolution: evaluation and debiasing methods. arXiv preprint arXiv:1804.06876 (2018)
Zheng, J., Chapman, W.W., Crowley, R.S., Savova, G.K.: Coreference resolution: a review of general methodologies and applications in the clinical domain. J. Biomed. Inform. 44(6), 1113–1122 (2011)
Article
Google Scholar
Zmigrod, R., Mielke, S.J., Wallach, H., Cotterell, R.: Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571 (2019)