Advancing Natural Language Processing: Beyond Embeddings
Abstract
Natural Language Processing (NLP) has advanced at an impressive pace over the past few years, largely due to the introduction of successful novel approaches to representing and understanding text. That is why the contextual embedding have turned out to be really strong paradigm in NLP. To summarize, we introduced contextual embeddings, and in upcoming articles, we will explore new trends in NLP that go beyond the standard approaches. 8, such as the work on contextual embeddings, in which the input is treated as a sequence of characters demarcated by vectors, and these contextual embeddings glean the context for the meaning of each token in a sentence at every moment. Stateoftheart performance has been achieved on the benchmark datasets, and for some tasks humanlevel performance could be obtained, by use of massive pretrained language models such as GPT (Generative Pretrained Transformer) and RoBERTa (Robustly optimized BERT approach).