  Event Argument extraction refers to the task of extracting structured
information from unstructured text for a particular event of interest. The
existing works exhibit poor capabilities to extract causal event arguments like
Reason and After Effects. Furthermore, most of the existing works model this
task at a sentence level, restricting the context to a local scope. While it
may be effective for short spans of text, for longer bodies of text such as
news articles, it has often been observed that the arguments for an event do
not necessarily occur in the same sentence as that containing an event trigger.
To tackle the issue of argument scattering across sentences, the use of global
context becomes imperative in this task. In our work, we propose an external
knowledge aided approach to infuse document-level event information to aid the
extraction of complex event arguments. We develop a causal network for our
event-annotated dataset by extracting relevant event causal structures from
ConceptNet and phrases from Wikipedia. We use the extracted event causal
features in a bi-directional transformer encoder to effectively capture
long-range inter-sentence dependencies. We report the effectiveness of our
proposed approach through both qualitative and quantitative analysis. In this
task, we establish our findings on an event annotated dataset in 5 Indian
languages. This dataset adds further complexity to the task by labelling
arguments of entity type (like Time, Place) as well as more complex argument
types (like Reason, After-Effect). Our approach achieves state-of-the-art
performance across all the five languages. Since our work does not rely on any
language-specific features, it can be easily extended to other languages.
