mlsanityData to 14 Oct 2026

XFEVER: Exploring Fact Verification across Languages

arXiv 2310.16278 ↗First posted 25 Oct 2023cs.CL cs.AIcited by 4

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Abstract (arXiv)

This paper introduces the Cross-lingual Fact Extraction and VERification (XFEVER) dataset designed for benchmarking the fact verification models across different languages. We constructed it by translating the claim and evidence texts of the Fact Extraction and VERification (FEVER) dataset into six languages. The training and development sets were translated using machine translation, whereas the test set includes texts translated by professional translators and machine-translated texts. Using the XFEVER dataset, two cross-lingual fact verification scenarios, zero-shot learning and translate-train learning, are defined, and baseline models for each scenario are also proposed in this paper. Experimental results show that the multilingual language model can be used to build fact verification models in different languages efficiently. However, the performance varies by language and is somewhat inferior to the English case. We also found that we can effectively mitigate model miscalibration by considering the prediction similarity between the English and target languages. The XFEVER dataset, code, and model checkpoints are available at https://github.com/nii-yamagishilab/xfever.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 25 Oct 2023.

Builds on

20 of 31 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  4. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
  5. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  6. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,202
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  9. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 742
  10. 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 464
  11. 2021A Survey on Automated Fact-Checkingcited by 378
  12. 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 225
8 more
  1. 2020XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generationcited by 222
  2. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
  3. 2019Fine-grained Fact Verification with Kernel Graph Attention Networkcited by 105
  4. 2021X-FACT: A New Benchmark Dataset for Multilingual Fact Checkingcited by 91
  5. 2021Towards Few-Shot Fact-Checking via Perplexitycited by 63
  6. 2021Consistency Regularization for Cross-Lingual Fine-Tuningcited by 41
  7. 2022Enhancing Cross-lingual Transfer by Manifold Mixupcited by 30
  8. 2021A Multi-Level Attention Model for Evidence-Based Fact Checkingcited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 3.

4 papers.

  1. 2024Do We Need Language-Specific Fact-Checking Models? The Case of Chinesecited by 15
  2. 2026FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Groundingcited by 0
  3. 2026AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languagescited by 0
  4. 2026Cross-lingual Biography Enrichment via Claim Extraction and Alignmentcited by 0