# Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models

arXiv: https://arxiv.org/abs/2009.02431 · HTML: https://mlsanity.com/paper/2009.02431/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 15 of 15)

- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [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)
- [LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content](https://mlsanity.com/paper/2101.11891/) (2021)
- [Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter](https://mlsanity.com/paper/2210.04710/) (2022)
- [From Chaos to Clarity: Claim Normalization to Empower Fact-Checking](https://mlsanity.com/paper/2310.14338/) (2023)
- [DESYR: Definition and Syntactic Representation Based Claim Detection on the Web](https://mlsanity.com/paper/2108.08759/) (2021)
- [On the Role of Images for Analyzing Claims in Social Media](https://mlsanity.com/paper/2103.09602/) (2021)
- [Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation](https://mlsanity.com/paper/2107.05684/) (2021)
- [Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media](https://mlsanity.com/paper/2204.10080/) (2022)
- [TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy](https://mlsanity.com/paper/2101.03529/) (2021)
- [Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through Claim Detection and Identification of Claim Spans](https://mlsanity.com/paper/2310.19267/) (2023)
- [Leveraging Social Discourse to Measure Check-worthiness of Claims for Fact-checking](https://mlsanity.com/paper/2309.09274/) (2023)
- [RAVE: Retrieval and Scoring Aware Verifiable Claim Detection](https://mlsanity.com/paper/2509.15793/) (2025)
- [ContextClaim: A Context-Driven Paradigm for Verifiable Claim Detection](https://mlsanity.com/paper/2603.30025/) (2026)

## Abstract (arXiv)

We introduce the strategies used by the Accenture Team for the CLEF2020 CheckThat! Lab, Task 1, on English and Arabic. This shared task evaluated whether a claim in social media text should be professionally fact checked. To a journalist, a statement presented as fact, which would be of interest to a large audience, requires professional fact-checking before dissemination. We utilized BERT and RoBERTa models to identify claims in social media text a professional fact-checker should review, and rank these in priority order for the fact-checker. For the English challenge, we fine-tuned a RoBERTa model and added an extra mean pooling layer and a dropout layer to enhance generalizability to unseen text. For the Arabic task, we fine-tuned Arabic-language BERT models and demonstrate the use of back-translation to amplify the minority class and balance the dataset. The work presented here was scored 1st place in the English track, and 1st, 2nd, 3rd, and 4th place in the Arabic track.