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 …

Causality represents the most important kind of correlation between events. Extracting
causali-ty from text has become a promising hot topic in NLP. However, there is no mature …

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

Causality extraction from natural language texts is a challenging open problem in artificial
intelligence. Existing methods utilize patterns, constraints, and machine learning techniques …

Causal knowledge is seen as one of the key ingredients to advance artificial intelligence.
Yet, few knowledge bases comprise causal knowledge to date, possibly due to significant …

We present a conditional text generation framework that posits sentential expressions of
possible causes and effects. This framework depends on two novel resources we develop in …

Acceptance testing is crucial to determine whether a system fulfills end-user requirements.
However, the creation of acceptance tests is a laborious task entailing two major …

In the agile domain, test cases are derived from acceptance criteria to verify the expected
system behavior. However, the design of test cases is laborious and has to be done …

This paper studies the end-to-end construction of an argumentation knowledge graph that is
intended to support argument synthesis, argumentative question answering, or fake news …

In this paper, we study the problem of answering questions of type “Could X cause Y?”
where X and Y are general phrases without any constraints. Answering such questions will …