# Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims

arXiv: https://arxiv.org/abs/2205.02596 · HTML: https://mlsanity.com/paper/2205.02596/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 66 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 30 of 35)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Combining Fact Extraction and Verification with Neural Semantic Matching Networks](https://mlsanity.com/paper/1811.07039/) (2018)
- [Fine-grained Fact Verification with Kernel Graph Attention Network](https://mlsanity.com/paper/1910.09796/) (2019)
- [Attention in Natural Language Processing](https://mlsanity.com/paper/1902.02181/) (2019)
- [GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://mlsanity.com/paper/1908.01843/) (2019)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [Reasoning Over Semantic-Level Graph for Fact Checking](https://mlsanity.com/paper/1909.03745/) (2019)
- [UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification](https://mlsanity.com/paper/1809.01479/) (2018)
- [BERT for Evidence Retrieval and Claim Verification](https://mlsanity.com/paper/1910.02655/) (2019)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research](https://mlsanity.com/paper/2006.05557/) (2020)
- [MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation](https://mlsanity.com/paper/2011.04088/) (2020)
- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset](https://mlsanity.com/paper/2008.00791/) (2020)
- [NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/2102.04567/) (2021)
- [Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository](https://mlsanity.com/paper/2002.00837/) (2020)

## Built on by (most cited first, 5 of 5)

- [Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks](https://mlsanity.com/paper/2405.05135/) (2024)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks](https://mlsanity.com/paper/2304.11411/) (2023)
- [Exploring Factual Entailment with NLI: A News Media Study](https://mlsanity.com/paper/2406.16842/) (2024)
- [PANACEA: An Automated Misinformation Detection System on COVID-19](https://mlsanity.com/paper/2303.01241/) (2023)

## Abstract (arXiv)

We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pandemic. We first describe the construction of the novel PANACEA dataset consisting of heterogeneous claims on COVID-19 and their respective information sources. The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims. We then propose novel techniques for automated veracity assessment based on Natural Language Inference including graph convolutional networks and attention based approaches. We have carried out experiments on evidence retrieval and veracity assessment on the dataset using the proposed techniques and found them competitive with SOTA methods, and provided a detailed discussion.