  Online debates involve a dynamic exchange of ideas over time, where
participants need to actively consider their opponents' arguments, respond with
counterarguments, reinforce their own points, and introduce more compelling
arguments as the discussion unfolds. Modeling such a complex process is not a
simple task, as it necessitates the incorporation of both sequential
characteristics and the capability to capture interactions effectively. To
address this challenge, we employ a sequence-graph approach. Building the
conversation as a graph allows us to effectively model interactions between
participants through directed edges. Simultaneously, the propagation of
information along these edges in a sequential manner enables us to capture a
more comprehensive representation of context. We also introduce a Sequence
Graph Attention layer to illustrate the proposed information update scheme. The
experimental results show that sequence graph networks achieve superior results
to existing methods in online debates.
