Large language models (LLMs) are gaining increasing popularity in both academia and
industry, owing to their unprecedented performance in various applications. As LLMs …

Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Pretraining large neural language models, such as BERT, has led to impressive gains on
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …

We introduce a new language representation model called BERT, which stands for
Bidirectional Encoder Representations from Transformers. Unlike recent language …

Fine-tuning large pretrained models is an effective transfer mechanism in NLP. However, in
the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Text embeddings are useful features in many applications such as semantic search and
computing text similarity. Previous work typically trains models customized for different use …

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

This paper presents a survey on hate speech detection. Given the steadily growing body of
social media content, the amount of online hate speech is also increasing. Due to the …

We propose a unified neural network architecture and learning algorithm that can be applied
to various natural language processing tasks including part-of-speech tagging, chunking …