Impact of 'Attention Is All You Need' in NLP: Transforming Natural Language Processing and Beyond
Keywords:
natural language processing, Self- Attention Layer, GPT, BERTAbstract
This research paper explores the significant influence of "Attention Is All You Need" in Natural Language Processing (NLP) and its broader impact on data science and artificial intelligence. The paper discusses how the Transformer architecture and self- attention mechanisms introduced in this work have revolutionized NLP tasks, including machine translation and text summarization. It also highlights the role of Transformers in pretrained language models like BERT and GPT. Moreover, the study emphasizes the versatility of the Transformer architecture, which has been successfully adapted in computer vision and speech recognition. The paper concludes by underlining how "Attention Is All You Need" continues to drive innovation and interdisciplinary research in NLP and AI.
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