  The aim of all Question Answering (QA) systems is to be able to generalize to
unseen questions. Current supervised methods are reliant on expensive data
annotation. Moreover, such annotations can introduce unintended annotator bias
which makes systems focus more on the bias than the actual task. In this work,
we propose Knowledge Triplet Learning (KTL), a self-supervised task over
knowledge graphs. We propose heuristics to create synthetic graphs for
commonsense and scientific knowledge. We propose methods of how to use KTL to
perform zero-shot QA and our experiments show considerable improvements over
large pre-trained transformer models.
