Can Language Models perform Abductive Commonsense Reasoning?
This is an incremental review and analysis for researchers in natural language processing and commonsense reasoning.
The paper reviews existing methodologies for abductive commonsense reasoning, re-implements baseline models, and analyzes weaknesses in current approaches, with code and results provided.
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.