# POSHAN: Cardinal POS Pattern Guided Attention for News Headline Incongruence

arXiv: https://arxiv.org/abs/2111.03547 · HTML: https://mlsanity.com/paper/2111.03547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder](https://mlsanity.com/paper/1811.07066/) (2018)
- [We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!](https://mlsanity.com/paper/1612.01340/) (2016)
- [Learning to Identify Ambiguous and Misleading News Headlines](https://mlsanity.com/paper/1705.06031/) (2017)
- [Breaking the News: First Impressions Matter on Online News](https://mlsanity.com/paper/1503.07921/) (2015)
- [Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks](https://mlsanity.com/paper/1710.01507/) (2017)
- [Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering](https://mlsanity.com/paper/1801.07853/) (2018)
- [MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching](https://mlsanity.com/paper/2010.03617/) (2020)

## Abstract (arXiv)

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.