A commonsense-infused language-agnostic learning framework for enhancing prediction of political bias in multilingual news headlines

Predicting the political bias of news headlines is a challenging task that becomes even more challenging in a multilingual setting with low-resource languages. To deal with this, we propose to utilise Inferential Commonsense Knowledge via a Translate–Retrieve–Translate strategy to introduce a learning framework. To begin with, we use the translate–retrieve–translate strategy to acquire inferential knowledge in the target language. We then employ an attention mechanism to emphasise important inferences. We finally integrate the attended inferences into a multilingual, pre-trained language model for the task of bias prediction. To evaluate the effectiveness of our framework, we present a dataset of over 62.6K multilingual news headlines annotated with their respective political biases in five low-resource European languages. We evaluate several state-of-the-art multilingual pre-trained language models since their …