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 …

As an essential component of human cognition, cause–effect relations appear frequently in
text, and curating cause–effect relations from text helps in building causal networks for …

Event causality identification (ECI) aims to identify causal relations of event mention pairs in
text. Despite achieving certain accomplishments, existing methods are still not effective due …

We study the problem of Event Causality Identification (ECI) to detect causal relation
between event mention pairs in text. Although deep learning models have recently shown …

Identifying causal relations of events is a crucial language understanding task. Despite
many efforts for this task, existing methods lack the ability to adopt background knowledge …

Document-level Event Causality Identification (DECI) aims to identify causal relations
between event pairs in a document. It poses a great challenge of across-sentence reasoning …

To understand event structures of documents, event causality identification (ECI) emerges
as a crucial task, aiming to discern causal relationships among event mentions. The latest …

Identifying the causal relations between events is an important task in natural language
processing (NLP). However, existing methods mainly leverage human empirical information …

The principle of independent causal mechanisms (ICM) states that generative processes of
real world data consist of independent modules which do not influence or inform each other …

Why-question answering (why-QA) is a task to retrieve answers (or answer passages) to
why-questions (eg," why are tsunamis generated?") from a text archive. Several previously …