Deep Learning models have shown success in a large variety of tasks by extracting
correlation patterns from high-dimensional data but still struggle when generalizing out of …

Over the past years, knowledge graph embedding approaches have proven effective for
knowledge graph completion tasks. However, most existing models are either built on a …

Abstract Document-level Event Causality Identification (DECI) aims to recognize causal
relations between events within a document. Recent studies focus on building a document …

As large language models (LLMs) grow larger and more sophisticated, assessing their"
reasoning" capabilities in natural language grows more challenging. Recent question …

Community question answering aims to connect queries and answers based on users'
community behaviors, find the most relevant solutions for newly raised questions, and …

Abstract Knowledge graphs (KGs) have achieved great success in many real applications,
and great efforts have been dedicated to constructing larger knowledge graphs. An obvious …

The objective of knowledge graph question answering is to assist users in answering
questions by utilizing the information stored within the graph. Users are not required to …

Cognitive diagnosis is an intelligent education task that aims to learn students' cognitive
states on knowledge concepts based on historical answering logs over questions. Existing …

Event Causality Identification (ECI) aims at determining whether there is a causal relation
between two event mentions. Conventional prompt learning designs a prompt template to …

The objective of knowledge graph question answering is to assist users in answering
questions by utilizing the information stored within the graph. Users are not required to …