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Predicting the political polarity of news headlines is a challenging taskthat becomes even more challenging in a multilingual setting with low-resourcelanguages. To deal with this, we propose to utilise the Inferential CommonsenseKnowledge via a Translate-Retrieve-Translate strategy to introduce a learningframework. To begin with, we use the method of translation and retrieval toacquire the inferential knowledge in the target language. We then employ anattention mechanism to emphasise important inferences. We finally integrate theattended inferences into a multilingual pre-trained language model for the taskof bias prediction. To evaluate the effectiveness of our framework, we presenta dataset of over 62.6K multilingual news headlines in five European languagesannotated with their respective political polarities. We evaluate severalstate-of-the-art multilingual pre-trained language models since theirperformance tends to vary across languages (low/high resource). Evaluationresults demonstrate that our proposed framework is effective regardless of themodels employed. Overall, the best performing model trained with only headlinesshow 0.90 accuracy and F1, and 0.83 jaccard score. With attended knowledge inour framework, the same model show an increase in 2.2% accuracy and F1, and3.6% jaccard score. Extending our experiments to individual languages revealsthat the models we analyze for Slovenian perform significantly worse than otherlanguages in our dataset. To investigate this, we assess the effect oftranslation quality on prediction performance. It indicates that the disparityin performance is most likely due to poor translation quality. We release ourdataset and scripts at: https://github.com/Swati17293/KG-Multi-Bias for futureresearch. Our framework has the potential to benefit journalists, socialscientists, news producers, and consumers.

A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlines