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