THE EVOLUTION OF EMBEDDINGS IN MACHINE LEARNING
Keywords:
Word Embeddings, Transformer Architecture, Contextual Representations, Cross-modal Embeddings, Bias Mitigation In AIAbstract
This comprehensive article explores the evolution of embeddings in machine learning, tracing their development from early techniques like Latent Semantic Analysis to state-of-the-art transformer-based models. It examines the paradigm shift brought about by Word2Vec, which revolutionized natural language processing by capturing complex semantic relationships in vector spaces. The article delves into advancements such as GloVe and FastText, highlighting their unique approaches to word representation. Furthermore, it investigates the expansion of embedding techniques beyond text, discussing applications in image processing with Convolutional Neural Networks, graph analysis through node2vec and Graph Neural Networks, and audio processing for speech and music. The groundbreaking impact of transformer architecture and models like BERT on contextual embeddings is thoroughly analyzed, along with their far-reaching implications for various NLP tasks. The article also addresses critical challenges facing embedding technology, including bias propagation, computational efficiency, and interpretability issues. Looking towards the future, it explores emerging directions such as cross-modal embeddings, efforts to mitigate bias, and potential applications in cutting-edge AI fields. Through this comprehensive examination, the article underscores the pivotal role of embeddings in advancing artificial intelligence and shaping its future trajectory
References
T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," arXiv preprint arXiv:1301.3781, 2013. [Online]. Available: https://arxiv.org/abs/1301.3781
S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, "Indexing by latent semantic analysis," Journal of the American Society for Information Science, vol. 41, no. 6, pp. 391-407, 1990. [Online]. Available: http://wordvec.colorado.edu/papers/Deerwester_1990.pdf
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," in Advances in Neural Information Processing Systems, 2013, pp. 3111-3119. [Online]. Available: https://proceedings.neurips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf
J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global Vectors for Word Representation," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1532-1543. [Online]. Available: https://aclanthology.org/D14-1162.pdf
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching Word Vectors with Subword Information," Transactions of the Association for Computational Linguistics, vol. 5, pp. 135-146, 2017. [Online]. Available: https://aclanthology.org/Q17-1010.pdf
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778. [Online]. Available: https://ieeexplore.ieee.org/document/7780459
A. Grover and J. Leskovec, "node2vec: Scalable Feature Learning for Networks," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855-864. [Online]. Available: https://dl.acm.org/doi/10.1145/2939672.2939754
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), 2019, pp. 4171-4186. [Online]. Available: https://aclanthology.org/N19-1423.pdf
T. Bolukbasi, K. W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai, "Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings," in Advances in Neural Information Processing Systems, 2016, pp. 4349-4357. [Online]. Available: https://proceedings.neurips.cc/paper/2016/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, "Learning Transferable Visual Models From Natural Language Supervision," in Proceedings of the 38th International Conference on Machine Learning, 2021, pp. 8748-8763. [Online]. Available: http://proceedings.mlr.press/v139/radford21a/radford21a.pdf
