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 chal-lenging 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 performance tends to vary across languages (low or high resource). Evaluation results demonstrate that our proposed framework is effective regardless of the models employed. Overall, the best-performing model trained with only headlines shows 0.90 accuracy and F1 and a 0.83 Jaccard score. With attended knowledge in our framework, the same model shows an increase in 2.2% accuracy and F1 and a 3.6% Jaccard score. Extending our experiments to individual languages reveals that the models we analyse for Slovenian perform significantly worse than other languages in our dataset. To investigate this, we assess the effect of translation quality on prediction performance. It indicates that the disparity in performance is most likely due to poor translation quality. We release our dataset and scripts at https://github.com/Swati17293/KG-Multi-Bias for future research. Our framework has the potential to benefit journalists, social scientists, news producers, and consumers.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).