BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.If you’d like to skip around, here are the papers we featured: Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries. To help you stay up to date with the latest breakthroughs in language modeling, we’ve summarized research papers featuring the key language models introduced during the last few years. While lots of AI experts agree with Anna Rogers’s statement that getting state-of-the-art results just by using more data and computing power is not research news, other NLP opinion leaders point out some positive moments in the current trend, like, for example, the possibility of seeing the fundamental limitations of the current paradigm.Īnyway, the latest improvements in NLP language models seem to be driven not only by the massive boosts in computing capacity but also by the discovery of ingenious ways to lighten models while maintaining high performance. Transfer learning and applying transformers to different downstream NLP tasks have become the main trend of the latest research advances.Īt the same time, there is a controversy in the NLP community regarding the research value of the huge pretrained language models occupying the leaderboards. The introduction of transfer learning and pretrained language models in natural language processing (NLP) pushed forward the limits of language understanding and generation. Check out Top 6 NLP Language Models Transforming AI in 2023. 7.4.UPDATE: We have published the updated version of this article, considering the latest research advances in large language models. 7.3.2 Vanishing Gradient Problem and Regularization. 7.3.1 Forward and Backpropagation in RNNs. 6.9.6 Exercises for Readers and Practitioners. 6.9.5 Understanding and Improving the Models. 6.9.3 Data Preprocessing and Data Splits. 6.7.1 Text Classification and Categorization. 6.4.2 Character-Based Representation and CNN. 6.3.2 Gradient with Respect to the Inputs ∂E∂X. 6.3.1 Gradient with Respect to Weights ∂E∂W. 6.2.2 Local Connectivity or Sparse Interactions. 6.2.1.2 The Convolution Operator and Its Properties. 5.4.5 Retrofitting with Semantic Lexicons. 5.2.4.7 word2vec Skip-gram: Forward and Backward Propagation. 5.2.4.6 word2vec CBOW: Forward and Backward Propagation. 4.8.7 Exercises for Readers and Practitioners. 4.8.5 Classifying with Unsupervised Features. 4.5.7.1 Computation and Memory Constraints. 4.3 Multilayer Perceptron (Neural Networks). 3.14.6 Exercises for Readers and Practitioners. 3.8.2.2 Subjectivity and Objectivity Detection. 2.9.3.2 Hyperparameter Search and Validation. 2.9.3.1 Feature Transformation and Reduction Impact. 2.8.3 Generative Approach: Conditional Random Fields. 2.8.2 Discriminative Approach: Hidden Markov Models. 2.7 Feature Transformation, Selection, and Dimensionality Reduction. 2.5.6 Practical Tips for Linear Algorithms. 2.4.6 Practical Tips for Machine Learning. 2.4.3.1 Classification Evaluation Metrics. 2.4.2 Generalization–Approximation Trade-off via the Bias–Variance Analysis. 2.4.1 Generalization–Approximation Trade-Off via the Vapnik–Chervonenkis Analysis. 1.4 Case Studies and Implementation Details. 1.2.3 Automatic Speech Recognition: A Brief History. 1.2.2 Natural Language Processing: A Brief History. Part I Machine Learning, NLP, and Speech Introduction.
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