  Causality understanding between events is a critical natural language
processing task that is helpful in many areas, including health care, business
risk management and finance. On close examination, one can find a huge amount
of textual content both in the form of formal documents or in content arising
from social media like Twitter, dedicated to communicating and exploring
various types of causality in the real world. Recognizing these "Cause-Effect"
relationships between natural language events continues to remain a challenge
simply because it is often expressed implicitly. Implicit causality is hard to
detect through most of the techniques employed in literature and can also, at
times be perceived as ambiguous or vague. Also, although well-known datasets do
exist for this problem, the examples in them are limited in the range and
complexity of the causal relationships they depict especially when related to
implicit relationships. Most of the contemporary methods are either based on
lexico-semantic pattern matching or are feature-driven supervised methods.
Therefore, as expected these methods are more geared towards handling explicit
causal relationships leading to limited coverage for implicit relationships and
are hard to generalize. In this paper, we investigate the language model's
capabilities for causal association among events expressed in natural language
text using sentence context combined with event information, and by leveraging
masked event context with in-domain and out-of-domain data distribution. Our
proposed methods achieve the state-of-art performance in three different data
distributions and can be leveraged for extraction of a causal diagram and/or
building a chain of events from unstructured text.
