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Pre-trained language models such as BERT have become a more common choice of natural language processing (NLP) tasks.
2013
Earlier work this paper cites.
Hasan, S., Curry, E.: Word re-embedding via manifold dimensionality retention. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP. pp. 321–326 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems, NIPS. pp. 5998–6008 (2017)
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Gao, J., He, D., Tan, X., Qin, T., Wang, L., Liu, T.: Representation degeneration problem in training natural language generation models. In: International Conference on Learning Representations, ICLR (2018)
2018
Earlier work this paper cites.
Gong, C., He, D., Tan, X., Qin, T., Wang, L., Liu, T.Y.: Frage: Frequency-agnostic word representation. In: Advances in neural information processing systems, NIPS. pp. 1334–1345 (2018)
2018
Earlier work this paper cites.
Mu, J., Viswanath, P.: All-but-the-top: Simple and effective post-processing for word representations. In: International Conference on Learning Representations, ICLR (2018)
2018
Cited alongside, same era.
Ethayarajh, K.: How contextual are contextualized word representations? comparing the geometry of bert, elmo, and gpt-2 embeddings. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, EMNLP. pp. 55–65 (2019)
2019
Cited alongside, same era.
Karve, S., Ungar, L., Sedoc, J.: Conceptor debiasing of word representations evaluated on weat. In: Proceedings of the First Workshop on Gender Bias in Natural Language Processing. pp. 40–48 (2019)
2019
Cited alongside, same era.
Liu, T., Ungar, L., Sedoc, J.: Unsupervised post-processing of word vectors via conceptor negation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 6778–6785 (2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Zhou, T., Sedoc, J., Rodu, J.: Getting in shape: word embedding subspaces. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, AAAI. pp. 5478–5484. AAAI Press (2019)
2019
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2020
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Wang, L., Huang, J., Huang, K., Hu, Z., Wang, G., Gu, Q.: Improving neural language generation with spectrum control. In: International Conference on Learning Representations, ICLR (2020)
2020
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Reif, E., Yuan, A., Wattenberg, M., Viegas, F.B., Coenen, A., Pearce, A., Kim, B.: Visualizing and measuring the geometry of bert. In: Advances in Neural Information Processing Systems, NIPS. pp. 8594–8603 (2019)
2019
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2020
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