  Predicting the political polarity 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 the Inferential Commonsense
Knowledge via a Translate-Retrieve-Translate strategy to introduce a learning
framework. To begin with, we use the method of translation and retrieval to
acquire the 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 in five European languages
annotated with their respective political polarities. We evaluate several
state-of-the-art multilingual pre-trained language models since their
performance tends to vary across languages (low/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
show 0.90 accuracy and F1, and 0.83 jaccard score. With attended knowledge in
our framework, the same model show an increase in 2.2% accuracy and F1, and
3.6% jaccard score. Extending our experiments to individual languages reveals
that the models we analyze 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.
