Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …

Text data augmentation is an effective strategy for overcoming the challenge of limited
sample sizes in many natural language processing (NLP) tasks. This challenge is especially …

Data annotation is a time-consuming and labor-intensive process for many NLP tasks.
Although there exist various methods to produce pseudo data labels, they are often task …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Large pre-trained language models (LMs) such as GPT-3 have acquired a surprising ability
to perform zero-shot learning. For example, to classify sentiment without any training …

Few-shot NLP research is highly active, yet conducted in disjoint research threads with
evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful …

State-of-the-art abstractive summarization models generally rely on extensive labeled data,
which lowers their generalization ability on domains where such data are not available. In …

Meta-learning has gained wide popularity as a training framework that is more data-efficient
than traditional machine learning methods. However, its generalization ability in complex …

Recent work has shown that large text-based neural language models, trained with
conventional supervised learning objectives, acquire a surprising propensity for few-and …