More than one hundred benchmarks have been developed to test the commonsense
knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems …

In the NLP community, recent years have seen a surge of research activities that address
machines' ability to perform deep language understanding which goes beyond what is …

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 …

Abstract Language communication plays an important role in human learning and
knowledge acquisition. With the emergence of a new generation of cognitive robots …

Existing video understanding datasets mostly focus on human interactions, with little
attention being paid to the" in the wild" settings, where the videos are recorded outdoors. We …

We present a method for extracting causality knowledge from Wikipedia, such as
Protectionism-> Trade war, where the cause and effect entities correspond to Wikipedia …

'Actions' play a vital role in how humans interact with the world and enable them to achieve
desired goals. As a result, most common sense (CS) knowledge for humans revolves …

Researchers in natural language processing are paying more attention to causality mining.
Numerous applications of the growing need for efficient and accurate causality mining …

It is important to learn whether text information remains valid or not for various applications
including story comprehension, information retrieval, and user state tracking on microblogs …

Learning contextual text embeddings that represent causal graphs has been useful in
improving the performance of downstream tasks like causal treatment effect estimation …