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Language models have become nearly ubiquitous in natural language processing applications achieving state-of-the-art results in many tasks including prosody.
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
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M. Honnibal and I. Montani, “spaCy 3: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing,” 2017, to appear
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G. Jawahar, B. Sagot, and D. Seddah, “What does bert learn about the structure of language?” in ACL 2019-57th Annual Meeting of the Association for Computational Linguistics , 2019
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M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “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) . New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 2227–2237. [Online]. Available: https://aclanthology.org/N18-1202
2018
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2018
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2018
Cited alongside, same era.
A. Talman, A. Suni, H. Celikkanat, S. Kakouros, J. Tiedemann, and M. Vainio, “Predicting prosodic prominence from text with pre-trained contextualized word representations,” in Proceedings of NoDaLiDa , 2019
2019
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2019
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2020
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A. Nayak and H. P. Timmapathini, “Using integrated gradients and constituency parse trees to explain linguistic acceptability learnt by bert,” in Proceedings of the 18th International Conference on Natural Language Processing (ICON) , 2021, pp. 80–85
2021
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