Pretraining on a large-scale corpus has become a standard method to build general
language models (LMs). Adapting a model to new data distributions targeting different …

Recently, foundation language models (LMs) have marked significant achievements in the
domains of natural language processing (NLP) and computer vision (CV). Unlike traditional …

Continual learning has become increasingly important as it enables NLP models to
constantly learn and gain knowledge over time. Previous continual learning methods are …

Continual learning (CL) is a machine learning paradigm that accumulates knowledge while
learning sequentially. The main challenge in CL is catastrophic forgetting of previously seen …

User intent detection is vital for understanding their demands in dialogue systems. Although
the User Intent Classification (UIC) task has been widely studied, for large-scale industrial …

Continual relation extraction (CRE) is an important task of continual learning, which aims to
learn incessantly emerging new relations between entities from texts. To avoid …

Lifelong seq2seq language generation models are trained with multiple domains in a
lifelong learning manner, with data from each domain being observed in an online fashion. It …

A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of
large annotated datasets with sufficient coverage of words in their diversity of senses. This …

In lifelong learning, a model learns different tasks sequentially throughout its lifetime. State-
of-the-art deep learning models, however, struggle to generalize in this setting and suffer …

In recent years, artificial intelligence has been widely used in modern construction, and
reinforcement learning methods have played an important role in it. The experience replay …