# BERT for Evidence Retrieval and Claim Verification

arXiv: https://arxiv.org/abs/1910.02655 · HTML: https://mlsanity.com/paper/1910.02655/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 30 of 57)

- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [GERE: Generative Evidence Retrieval for Fact Verification](https://mlsanity.com/paper/2204.05511/) (2022)
- [Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models](https://mlsanity.com/paper/2305.14623/) (2023)
- [Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification](https://mlsanity.com/paper/2110.15116/) (2021)
- [Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter](https://mlsanity.com/paper/2210.04710/) (2022)
- [Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection](https://mlsanity.com/paper/2102.02680/) (2021)
- [From Chaos to Clarity: Claim Normalization to Empower Fact-Checking](https://mlsanity.com/paper/2310.14338/) (2023)
- [Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification](https://mlsanity.com/paper/2106.01191/) (2021)
- [A Multi-Level Attention Model for Evidence-Based Fact Checking](https://mlsanity.com/paper/2106.00950/) (2021)
- [Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction](https://mlsanity.com/paper/2207.14116/) (2022)
- [BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification](https://mlsanity.com/paper/2303.16974/) (2023)
- [CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-Checking](https://mlsanity.com/paper/2408.08535/) (2024)
- [Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media](https://mlsanity.com/paper/2310.18205/) (2023)
- [BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking](https://mlsanity.com/paper/2502.16181/) (2025)
- [Mask More and Mask Later: Efficient Pre-training of Masked Language Models by Disentangling the [MASK] Token](https://mlsanity.com/paper/2211.04898/) (2022)
- [Can LLMs Automate Fact-Checking Article Writing?](https://mlsanity.com/paper/2503.17684/) (2025)
- [Knowledge-Enhanced Evidence Retrieval for Counterargument Generation](https://mlsanity.com/paper/2109.09057/) (2021)
- [CFEVER: A Chinese Fact Extraction and VERification Dataset](https://mlsanity.com/paper/2402.13025/) (2024)
- [Hidden Biases in Unreliable News Detection Datasets](https://mlsanity.com/paper/2104.10130/) (2021)
- [Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims](https://mlsanity.com/paper/2205.02596/) (2022)
- [ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources](https://mlsanity.com/paper/2210.14846/) (2022)
- [Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables](https://mlsanity.com/paper/2402.13028/) (2024)
- [Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning](https://mlsanity.com/paper/2510.01932/) (2025)
- [Artefact Retrieval: Overview of NLP Models with Knowledge Base Access](https://mlsanity.com/paper/2201.09651/) (2022)
- [The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation](https://mlsanity.com/paper/2204.02000/) (2022)
- [Investigating Zero- and Few-shot Generalization in Fact Verification](https://mlsanity.com/paper/2309.09444/) (2023)
- [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)
- [ChronoFact: Timeline-based Temporal Fact Verification](https://mlsanity.com/paper/2410.14964/) (2024)
- [A Graph-based Verification Framework for Fact-Checking](https://mlsanity.com/paper/2503.07282/) (2025)

## Abstract (arXiv)

Motivated by the promising performance of pre-trained language models, we investigate BERT in an evidence retrieval and claim verification pipeline for the FEVER fact extraction and verification challenge. To this end, we propose to use two BERT models, one for retrieving potential evidence sentences supporting or rejecting claims, and another for verifying claims based on the predicted evidence sets. To train the BERT retrieval system, we use pointwise and pairwise loss functions, and examine the effect of hard negative mining. A second BERT model is trained to classify the samples as supported, refuted, and not enough information. Our system achieves a new state of the art recall of 87.1 for retrieving top five sentences out of the FEVER documents consisting of 50K Wikipedia pages, and scores second in the official leaderboard with the FEVER score of 69.7.