  We present two new datasets and a novel attention mechanism for Natural
Language Inference (NLI). Existing neural NLI models, even though when trained
on existing large datasets, do not capture the notion of entity and role well
and often end up making mistakes such as "Peter signed a deal" can be inferred
from "John signed a deal". The two datasets have been developed to mitigate
such issues and make the systems better at understanding the notion of
"entities" and "roles". After training the existing architectures on the new
dataset we observe that the existing architectures does not perform well on one
of the new benchmark. We then propose a modification to the "word-to-word"
attention function which has been uniformly reused across several popular NLI
architectures. The resulting architectures perform as well as their unmodified
counterparts on the existing benchmarks and perform significantly well on the
new benchmark for "roles" and "entities".
