Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

The common practice for training commonsense models has gone from-human-to-corpus-to-
machine: humans author commonsense knowledge graphs in order to train commonsense …

Storytelling and narrative are fundamental to human experience, intertwined with our social
and cultural engagement. As such, researchers have long attempted to create systems that …

The new generation of state-of-the-art computer vision systems are trained from natural
language supervision, ranging from simple object category names to descriptive captions …

Recent years have brought about a renewed interest in commonsense representation and
reasoning in the field of natural language understanding. The development of new …

With the increasing of model capacity brought by pre-trained language models, there
emerges boosting needs for more knowledgeable natural language processing (NLP) …

The diverse relationships among real-world events, including coreference, temporal, causal,
and subevent relations, are fundamental to understanding natural languages. However, two …

Event relations are crucial for narrative understanding and reasoning. Governed by nuanced
logic, event relation extraction (ERE) is a challenging task that demands thorough semantic …

Structured knowledge is important for many AI applications. Commonsense knowledge,
which is crucial for robust human-centric AI, is covered by a small number of structured …