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Full-Text Articles in Physical Sciences and Mathematics
Enhancing Zero-Shot And Few-Shot Text Classification Using Pre-Trained Language Models, Yanan Chen
Enhancing Zero-Shot And Few-Shot Text Classification Using Pre-Trained Language Models, Yanan Chen
Theses and Dissertations (Comprehensive)
In recent years, the community of natural language processing (NLP) has seen amazing progress in the development of pre-trained language models (PLMs). The novel paradigm of PLMs does not require labeled data, allowing us to experiment with increased training scale through employing freely available colossal online self-training corpus to push the limits. Language models (LMs), such as GPT, BERT and T5, have achieved high performance on a wide range of NLP tasks. Meanwhile, research on zero-shot and few-shot text classification has received increasing attention. As labelling can be costly and time-consuming, how to perform data augmentation (DA) and enhance the …
Explainable Neural Attention Recommender Systems, Omer Tal
Explainable Neural Attention Recommender Systems, Omer Tal
Theses and Dissertations (Comprehensive)
Recommender systems, predictive models that provide lists of personalized suggestions, have become increasingly popular in many web-based businesses. By presenting potential items that may interest a user, these systems are able to better monetize and improve users’ satisfaction. In recent years, the most successful approaches rely on capturing what best define users and items in the form of latent vectors, a numeric representation that assumes all instances can be described by their respective affiliation towards a set of hidden features. However, recommendation methods based on latent features still face some realworld limitations. The data sparsity problem originates from the unprecedented …