Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

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

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …

Large-scale pre-trained language models have contributed significantly to natural language
processing by demonstrating remarkable abilities as few-shot learners. However, their …

Neural models have achieved remarkable success on relation extraction (RE) benchmarks.
However, there is no clear understanding which type of information affects existing RE …

Pre-trained Language Models (PLMs) have shown superior performance on various
downstream Natural Language Processing (NLP) tasks. However, conventional pre-training …

Relational facts are an important component of human knowledge, which are hidden in vast
amounts of text. In order to extract these facts from text, people have been working on …

Relation extraction (RE) is a fundamental process in constructing knowledge graphs.
However, previous methods on relation extraction suffer sharp performance decline in short …

Narratives are present in many forms of human expression and can be understood as a
fundamental way of communication between people. Computational understanding of the …

The introduction of Transformer neural networks has changed the landscape of Natural
Language Processing during the last three years. While models inspired by it have …