# Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation

arXiv: https://arxiv.org/abs/2107.05684 · HTML: https://mlsanity.com/paper/2107.05684/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media](https://mlsanity.com/paper/2007.13184/) (2020)
- [A Survey of Deep Learning for Scientific Discovery](https://mlsanity.com/paper/2003.11755/) (2020)
- [Load What You Need: Smaller Versions of Multilingual BERT](https://mlsanity.com/paper/2010.05609/) (2020)
- [Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models](https://mlsanity.com/paper/2009.02431/) (2020)

## Built on by (most cited first, 6 of 6)

- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News](https://mlsanity.com/paper/2109.12987/) (2021)
- [Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification](https://mlsanity.com/paper/2212.13939/) (2022)
- [Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter](https://mlsanity.com/paper/2211.05087/) (2022)
- [Check-worthy Claim Detection across Topics for Automated Fact-checking](https://mlsanity.com/paper/2212.08514/) (2022)
- [Leveraging Social Discourse to Measure Check-worthiness of Claims for Fact-checking](https://mlsanity.com/paper/2309.09274/) (2023)

## Abstract (arXiv)

This paper discusses the approach used by the Accenture Team for CLEF2021 CheckThat! Lab, Task 1, to identify whether a claim made in social media would be interesting to a wide audience and should be fact-checked. Twitter training and test data were provided in English, Arabic, Spanish, Turkish, and Bulgarian. Claims were to be classified (check-worthy/not check-worthy) and ranked in priority order for the fact-checker. Our method used deep neural network transformer models with contextually sensitive lexical augmentation applied on the supplied training datasets to create additional training samples. This augmentation approach improved the performance for all languages. Overall, our architecture and data augmentation pipeline produced the best submitted system for Arabic, and performance scales according to the quantity of provided training data for English, Spanish, Turkish, and Bulgarian. This paper investigates the deep neural network architectures for each language as well as the provided data to examine why the approach worked so effectively for Arabic, and discusses additional data augmentation measures that should could be useful to this problem.