  Finding counterevidence to statements is key to many tasks, including
counterargument generation. We build a system that, given a statement,
retrieves counterevidence from diverse sources on the Web. At the core of this
system is a natural language inference (NLI) model that determines whether a
candidate sentence is valid counterevidence or not. Most NLI models to date,
however, lack proper reasoning abilities necessary to find counterevidence that
involves complex inference. Thus, we present a knowledge-enhanced NLI model
that aims to handle causality- and example-based inference by incorporating
knowledge graphs. Our NLI model outperforms baselines for NLI tasks, especially
for instances that require the targeted inference. In addition, this NLI model
further improves the counterevidence retrieval system, notably finding complex
counterevidence better.
