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Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context.
Moosavi, N.S., Strube, M.: Lexical Features in Coreference Resolution: To be Used With Caution. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. pp. 14–19 (2017), https://www.aclweb.org/anthology/P17-2003
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Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., Fidler, S.: Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 19–27 (2015), https://www.cv-foundation.org/openaccess/content˙iccv˙2015/papers/Zhu˙Aligning˙Books˙and˙ICCV˙2015˙paper.pdf
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Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural Architectures for Named Entity Recognition. In: Proceedings of NAACL-HLT 2016. pp. 260–270 (2016), https://www.aclweb.org/anthology/N16-1030
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Augenstein, I., Derczynski, L., Bontcheva, K.: Generalisation in named entity recognition: A quantitative analysis. Computer Speech & Language 44
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Derczynski, L., Nichols, E., Van Erp, M., Limsopatham, N.: Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition. In: 3rd Workshop on Noisy User-generated Text. pp. 140–147 (2017), https://www.aclweb.org/anthology/W17-4418
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Moosavi, N.S., Strube, M.: Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 193–203 (2018), http://aclweb.org/anthology/D18-1018
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Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). pp. 2227–2237 (2 2018), http://www.aclweb.org/anthology/N18-1202
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2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need. In: Advances in Neural Information Processing Systems. pp. 5998–6008 (2017), https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
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Akbik, A., Blythe, D., Vollgraf, R.: Contextual string embeddings for sequence labeling. In: Proceedings of the 27th International Conference on Computational Linguistics. pp. 1638–1649 (2018), http://www.aclweb.org/anthology/C18-1139
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Howard, J., Ruder, S.: Universal Language Model Fine-tuning for Text Classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. pp. 328–339 (2018), http://aclweb.org/anthology/P18-1031
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186 (2019), https://www.aclweb.org/anthology/N19-1423
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Pires, T., Schlinger, E., Garrette, D.: How multilingual is Multilingual BERT? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 4996–5001 (2019), https://www.aclweb.org/anthology/P19-1493
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