Semantic representation is receiving growing attention in NLP in the past few years, and
many proposals for semantic schemes (eg, AMR, UCCA, GMB, UDS) have been put forth …

Causal reasoning ability is crucial for numerous NLP applications. Despite the impressive
emerging ability of ChatGPT in various NLP tasks, it is unclear how well ChatGPT performs …

In the field of artificial intelligence (AI), the quest to understand and model data-generating
processes (DGPs) is of paramount importance. Deep generative models (DGMs) have …

Causal relation extraction is a challenging yet very important task for Natural Language
Processing (NLP). There are many existing approaches developed to tackle this task, either …

Identifying causal relations of events is an important task in natural language processing
area. However, the task is very challenging, because event causality is usually expressed in …

There have been a wide range of recent annotated corpora concerning events, either
regarding event coreference, the temporal order of events, hierarchical “subevent” structure …

The diverse relationships among real-world events, including coreference, temporal, causal,
and subevent relations, are fundamental to understanding natural languages. However, two …

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 …

Despite the importance of understanding causality, corpora addressing causal relations are
limited. There is a discrepancy between existing annotation guidelines of event causality …

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 …