  Pre-trained Language Models (PTLMs) have been shown to perform well on
natural language tasks. Many prior works have leveraged structured commonsense
present in the form of entities linked through labeled relations in Knowledge
Graphs (KGs) to assist PTLMs. Retrieval approaches use KG as a separate static
module which limits coverage since KGs contain finite knowledge. Generative
methods train PTLMs on KG triples to improve the scale at which knowledge can
be obtained. However, training on symbolic KG entities limits their
applicability in tasks involving natural language text where they ignore
overall context. To mitigate this, we propose a CommonSense Contextualizer
(CoSe-Co) conditioned on sentences as input to make it generically usable in
tasks for generating knowledge relevant to the overall context of input text.
To train CoSe-Co, we propose a novel dataset comprising of sentence and
commonsense knowledge pairs. The knowledge inferred by CoSe-Co is diverse and
contain novel entities not present in the underlying KG. We augment generated
knowledge in Multi-Choice QA and Open-ended CommonSense Reasoning tasks leading
to improvements over current best methods on CSQA, ARC, QASC and OBQA datasets.
We also demonstrate its applicability in improving performance of a baseline
model for paraphrase generation task.
