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[Re] A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space

Authors: Benjamin Džubur;

[Re] A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space

Abstract

Replication

Keywords

python, machine learning, rescience c, natural language processing

16 references, page 1 of 2

1. J. Mu and P. Viswanath. “All-but-the-Top: Simple and Effective Postprocessing for Word Representations.” In: International Conference on Learning Representations. 2018. URL: https : / / openreview . net / forum ? id = HkuGJ3kCb.

2. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. “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). Minneapolis, Minnesota: Association for Computational Linguistics, June 2019, pp. 4171-4186. DOI: 10.18653/v1/N19- 1423. URL: https://aclanthology.org/N19-1423.

3. A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever. “Language Models are Unsupervised Multitask Learners.” In: 2019.

4. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. RoBERTa: A Robustly Optimized BERT Pretraining Approach. 2019. arXiv:1907.11692 [cs.CL].

5. J. Gao, D. He, X. Tan, T. Qin, L. Wang, and T. Liu. “Representation Degeneration Problem in Training Natural Language Generation Models.” In: CoRR abs/1907.12009 (2019). arXiv:1907.12009. URL: http://arxiv.org/abs/ 1907.12009.

6. S. Rajaee and M. T. Pilehvar. “A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space.” In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Online: Association for Computational Linguistics, Aug. 2021, pp. 575-584. DOI: 10.18653/v1/2021.acl-short.73. URL: https: //aclanthology.org/2021.acl-short.73. [OpenAIRE]

7. A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman. “GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.” In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Brussels, Belgium: Association for Computational Linguistics, Nov. 2018, pp. 353-355. DOI: 10.18653/v1/W18-5446. URL: https://aclanthology.org/W18-5446.

8. A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems.” In: CoRR abs/1905.00537 (2019). arXiv:1905.00537. URL: http://arxiv.org/abs/1905.00537.

9. E. Agirre, D. Cer, M. Diab, and A. Gonzalez-Agirre. “SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity.” In: *SEM 2012: The First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012). Montréal, Canada: Association for Computational Linguistics, July 2012, pp. 385-393. URL: https://aclanthology.org/S12-1051.

10. E. Agirre, D. Cer, M. Diab, A. Gonzalez-Agirre, and W. Guo. “*SEM 2013 shared task: Semantic Textual Similarity.” In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity. Atlanta, Georgia, USA: Association for Computational Linguistics, June 2013, pp. 32-43. URL: https://aclanthology.org/S13-1004.

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