# Automated Fact-Checking in Dialogue: Are Specialized Models Needed?

arXiv: https://arxiv.org/abs/2311.08195 · HTML: https://mlsanity.com/paper/2311.08195/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [DialFact: A Benchmark for Fact-Checking in Dialogue](https://mlsanity.com/paper/2110.08222/) (2021)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues](https://mlsanity.com/paper/2105.09914/) (2021)
- [Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation](https://mlsanity.com/paper/2205.13928/) (2022)

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

- [Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts](https://mlsanity.com/paper/2505.18677/) (2025)
- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)
- [Context-Aware Multimodal Claim Verification in Spoken Dialogues](https://mlsanity.com/paper/2606.11420/) (2026)
- [LoraMap: Harnessing the Power of LoRA Connections](https://mlsanity.com/paper/2408.16264/) (2024)
- [BiCon-Gate: Consistency-Gated De-colloquialisation for Dialogue Fact-Checking](https://mlsanity.com/paper/2604.14389/) (2026)

## Abstract (arXiv)

Prior research has shown that typical fact-checking models for stand-alone claims struggle with claims made in dialogues. As a solution, fine-tuning these models on labelled dialogue data has been proposed. However, creating separate models for each use case is impractical, and we show that fine-tuning models for dialogue results in poor performance on typical fact-checking. To overcome this challenge, we present techniques that allow us to use the same models for both dialogue and typical fact-checking. These mainly focus on retrieval adaptation and transforming conversational inputs so that they can be accurately predicted by models trained on stand-alone claims. We demonstrate that a typical fact-checking model incorporating these techniques is competitive with state-of-the-art models fine-tuned for dialogue, while maintaining its accuracy on stand-alone claims.