  Abductive Reasoning is a task of inferring the most plausible hypothesis
given a set of observations. In literature, the community has approached to
solve this challenge by classifying/generating a likely hypothesis that does
not contradict with a past observation and future observation. Some of the most
well-known benchmarks that tackle this problem are aNLI and aNLG (pronounced as
alpha-NLI and alpha-NLG). In this report, I review over some of the
methodologies that were attempted to solve this challenge, re-implement the
baseline models, and analyze some of the weaknesses that current approaches
have. The code and the re-implemented results are available at this link.
