POSHAN: Cardinal POS Pattern Guided Attention for News Headline Incongruence
This addresses the reliability of web news by improving detection of misleading headlines with cardinal values, though it is incremental as it focuses on a specific case within an existing problem.
The paper tackled the problem of detecting click-bait and incongruent news headlines, especially when they contain contextually important cardinal values like quantities, and proposed POSHAN, a neural attention-based method that outperformed all baselines on two public datasets.
Automatic detection of click-bait and incongruent news headlines is crucial to maintaining the reliability of the Web and has raised much research attention. However, most existing methods perform poorly when news headlines contain contextually important cardinal values, such as a quantity or an amount. In this work, we focus on this particular case and propose a neural attention based solution, which uses a novel cardinal Part of Speech (POS) tag pattern based hierarchical attention network, namely POSHAN, to learn effective representations of sentences in a news article. In addition, we investigate a novel cardinal phrase guided attention, which uses word embeddings of the contextually-important cardinal value and neighbouring words. In the experiments conducted on two publicly available datasets, we observe that the proposed methodgives appropriate significance to cardinal values and outperforms all the baselines. An ablation study of POSHAN shows that the cardinal POS-tag pattern-based hierarchical attention is very effective for the cases in which headlines contain cardinal values.