arxiv: 2604.09812 · v2 · submitted 2026-04-10 · 💻 cs.CL

Recognition: unknown

Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering

Rrubaa Panchendrarajan , Arkaitz Zubiaga

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:51 UTC · model grok-4.3

classification 💻 cs.CL

keywords multilingual embeddingsclaim clusteringfact-checkingcontrastive learningmisinformationsemantic similaritycross-lingual transfer

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The pith

Claim2Vec fine-tunes multilingual encoders on similar claim pairs to improve clustering of recurring fact-check claims.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper addresses the challenge of grouping similar claims that recur in misinformation, so that one fact-check can cover many instances, especially when claims appear in different languages. It introduces Claim2Vec by taking an existing multilingual encoder and fine-tuning it with contrastive learning that brings embeddings of similar claim pairs closer together while separating dissimilar ones. Experiments on three datasets using fourteen embedding models and seven clustering algorithms show that the resulting embeddings produce clusters with better label alignment and stronger geometric structure than the base models. The gains appear particularly in clusters that mix languages, indicating that the fine-tuning transfers knowledge across languages. This approach matters because automated fact-checking systems could then handle high volumes of repeated claims without checking each one individually.

Core claim

Optimizing a multilingual encoder through contrastive learning on pairs of similar multilingual claims produces vector embeddings that improve claim clustering performance, specifically by increasing cluster label alignment and enhancing the geometric structure of the embedding space across multiple cluster configurations and datasets.

What carries the argument

Contrastive learning on similar multilingual claim pairs, which refines the embedding space so that claims resolvable by the same fact-check are placed closer together regardless of language.

If this is right

Claims that share a fact-check can be grouped more reliably even when they appear in different languages.
Multilingual clusters show measurable gains from cross-lingual transfer during fine-tuning.
The improvements hold across different clustering algorithms and varying numbers of clusters.
Standard multilingual encoders without this specific fine-tuning produce weaker cluster structures on the same tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Fact-checking pipelines could reduce redundant checks by routing entire clusters to a single verification step.
The method might extend to other text clustering domains where recurring items need to be grouped across languages, such as social media topics.
Pairing the embeddings with retrieval systems could further speed up matching new claims to existing fact-checks.

Load-bearing premise

The pairs of similar multilingual claims used for contrastive fine-tuning are accurately labeled and representative of real-world recurring claims.

What would settle it

Clustering performance fails to improve, or the geometric structure does not tighten, when the model is tested on new claim datasets whose recurring patterns were not represented in the fine-tuning pairs.

Figures

Figures reproduced from arXiv: 2604.09812 by Arkaitz Zubiaga, Rrubaa Panchendrarajan.

**Figure 2.** Figure 2: Methodology of Claim2Vec learning and claim clustering (a) Positive Pairs (b) Negative Pairs [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Positive and negative pairs’ cosine distance [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: ARI, AMI, and Silhouette Score (SS) vs Number of Clusters Produced [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Heatmap of Split and Merge Error Rates Across Languages in [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Multilingual vs Monolingual Gain (%) 5.3.3 Multilingual Performance We assess fine-tuning with multilingual data by analyzing clusters that contain claims written in more than one language. Specifically, we compute the performance gain—defined as the difference between Claim2Vec and BGE-M3—for two cluster types: (i) monolingual clusters, which contain claims in a single language, and (ii) multilingual … view at source ↗

**Figure 7.** Figure 7: 2D Projection of Two Topic Groups in MultiClaim [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗

**Figure 8.** Figure 8: Top 30 Frequent Words of Two Topic Groups in [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

**Figure 10.** Figure 10: 2D projection of claims in ClaimMatch with the red color data points highlighting claims belonging to the split and mismerge error types [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗

read the original abstract

Recurrent claims present a major challenge for automated fact-checking systems designed to combat misinformation, especially in multilingual settings. While tasks such as claim matching and fact-checked claim retrieval aim to address this problem by linking claim pairs, the broader challenge of effectively representing groups of similar claims that can be resolved with the same fact-check via claim clustering remains relatively underexplored. To address this gap, we introduce Claim2Vec, the first multilingual embedding model optimized to represent fact-check claims as vectors in an improved semantic embedding space. We fine-tune a multilingual encoder using contrastive learning with similar multilingual claim pairs. Experiments on the claim clustering task using three datasets, 14 multilingual embedding models, and 7 clustering algorithms demonstrate that Claim2Vec significantly improves clustering performance. Specifically, it enhances both cluster label alignment and the geometric structure of the embedding space across different cluster configurations. Our multilingual analysis shows that clusters containing multiple languages benefit from fine-tuning, demonstrating cross-lingual knowledge transfer.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Claim2Vec fine-tunes a multilingual encoder on similar claim pairs and gets measurable clustering gains, but the results stand or fall on how those pairs were sourced and validated.

read the letter

The paper's core contribution is Claim2Vec, a contrastively fine-tuned multilingual model aimed at embedding fact-check claims so that groups sharing the same fact-check cluster together more cleanly. They test this on three datasets using 14 base encoders and 7 clustering algorithms, and report better label alignment plus tighter geometric structure in the embedding space, with extra benefit when clusters span languages. That focus on clustering rather than simple pairwise matching is the actual step forward, and the scale of the comparisons gives a practical sense of where the fine-tuning helps versus off-the-shelf models. The cross-lingual transfer observation is also useful for anyone dealing with real multilingual misinformation streams. The main soft spot is the training data itself. The abstract and stress-test note leave open exactly how the similar multilingual claim pairs were identified, labeled, and filtered. If the construction used automatic matching, shared metadata without noise checks, or any source overlap with the evaluation sets, the reported improvements could partly reflect leakage or domain match instead of better semantics. The lack of detail on statistical significance, error bars, or preprocessing in the summary also means the strength of the gains is still moderate until the full tables and methods are checked. This work is aimed at researchers building or evaluating tools for automated fact-checking and multilingual claim analysis. Anyone already running embedding experiments for clustering tasks will get concrete baselines and a clear recipe to try. It has enough new empirical ground and a focused task to merit a serious referee, provided the pair-construction pipeline and significance tests are examined closely. I would send it to review with that specific request for clarification.

Referee Report

2 major / 2 minor

Summary. The paper introduces Claim2Vec, the first multilingual embedding model for fact-check claims, obtained by contrastive fine-tuning of a multilingual encoder on similar multilingual claim pairs. It evaluates the approach on a claim clustering task across three datasets, 14 multilingual embedding models, and 7 clustering algorithms, claiming significant gains in cluster label alignment and geometric structure of the embedding space, with additional benefits for multilingual clusters via cross-lingual transfer.

Significance. If the reported clustering improvements hold after rigorous validation of the training pairs and statistical controls, the work would be significant for multilingual automated fact-checking by enabling better grouping of recurrent claims that share fact-checks. The comparative experimental design across multiple models, algorithms, and datasets is a strength, as is the focus on an underexplored clustering formulation rather than pairwise matching.

major comments (2)

[Methods / Data preparation] The construction, sourcing, labeling, and validation of the similar multilingual claim pairs used for contrastive fine-tuning are not described in sufficient detail (see abstract and any methods section on data preparation). This is load-bearing for the central claim, as unvalidated automatic matching, shared metadata without noise filtering, or overlap with the three evaluation datasets could produce gains via label leakage or domain match rather than semantic improvements in the embedding space.
[Experiments / Results] The experimental results claim that Claim2Vec 'significantly improves' clustering performance across 14 models and 7 algorithms (abstract), yet no details are provided on statistical significance testing, error bars, variance across runs, or preprocessing steps. This undermines assessment of whether the gains in label alignment and geometric structure are robust or could be explained by confounds.

minor comments (2)

[Abstract] The abstract states that clusters with multiple languages 'benefit from fine-tuning' but does not specify the exact multilingual analysis method or metrics used to demonstrate cross-lingual knowledge transfer.
[Methods] Notation for the contrastive loss and embedding objectives could be clarified with explicit equations to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below and have prepared revisions to improve the manuscript's clarity and rigor.

read point-by-point responses

Referee: [Methods / Data preparation] The construction, sourcing, labeling, and validation of the similar multilingual claim pairs used for contrastive fine-tuning are not described in sufficient detail (see abstract and any methods section on data preparation). This is load-bearing for the central claim, as unvalidated automatic matching, shared metadata without noise filtering, or overlap with the three evaluation datasets could produce gains via label leakage or domain match rather than semantic improvements in the embedding space.

Authors: We agree that the current description of the training pair construction is insufficiently detailed. The revised manuscript will include an expanded methods subsection that specifies: (1) the exact sources and collection process for the multilingual claim pairs, (2) the similarity labeling procedure (including any automated matching rules and subsequent manual or semi-automated validation steps), (3) noise-filtering criteria applied to the pairs, and (4) explicit verification that no claim pairs overlap with the three evaluation datasets used for clustering. These additions will allow readers to assess whether the reported gains stem from genuine semantic improvements rather than leakage or domain artifacts. revision: yes
Referee: [Experiments / Results] The experimental results claim that Claim2Vec 'significantly improves' clustering performance across 14 models and 7 algorithms (abstract), yet no details are provided on statistical significance testing, error bars, variance across runs, or preprocessing steps. This undermines assessment of whether the gains in label alignment and geometric structure are robust or could be explained by confounds.

Authors: We acknowledge the need for greater statistical transparency. In the revision we will: (a) describe all preprocessing steps in detail, (b) report performance metrics with error bars derived from multiple independent runs (different random seeds for clustering initialization and fine-tuning), (c) include statistical significance tests (e.g., paired t-tests or non-parametric equivalents with corrected p-values) comparing Claim2Vec against the strongest baselines, and (d) discuss observed variance across the 7 algorithms and 3 datasets. These changes will allow a more rigorous evaluation of the robustness of the improvements. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical fine-tuning and external evaluation

full rationale

The paper introduces Claim2Vec via contrastive fine-tuning of a multilingual encoder on similar claim pairs, then evaluates clustering performance on three external datasets using 14 models and 7 algorithms. No equations, derivations, or predictions are presented that reduce to fitted parameters or self-referential definitions by construction. All central claims rest on comparative experimental results against baselines, with no self-citation load-bearing the core argument or uniqueness imported from prior author work. The work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that contrastive fine-tuning on claim pairs produces meaningfully improved embeddings for clustering, plus standard assumptions about the validity of the chosen datasets and evaluation metrics.

free parameters (1)

contrastive learning hyperparameters
Fine-tuning involves choices for learning rate, batch size, and loss margins that are not specified in the abstract but affect the resulting embeddings.

axioms (1)

domain assumption Contrastive learning on similar claim pairs yields embeddings with improved geometric structure for clustering
Invoked when claiming that fine-tuning enhances cluster label alignment and embedding space structure.

pith-pipeline@v0.9.0 · 5468 in / 1288 out tokens · 41910 ms · 2026-05-10T16:51:26.384002+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

6 extracted references · 3 canonical work pages · 2 internal anchors

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InEuropean conference on information retrieval, pages 367–381

Did i see it before? detecting previously- checked claims over twitter. InEuropean conference on information retrieval, pages 367–381. Springer. Watheq Mansour, Tamer Elsayed, and Abdulaziz Al- Ali. 2023. This is not new! spotting previously- verified claims over twitter.Information Processing & Management, 60(4):103414. Leland McInnes, John Healy, Steve ...

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work page arXiv 2018
[3]

Multilingual E5 Text Embeddings: A Technical Report

Sciclops: Detecting and contextualizing sci- entific claims for assisting manual fact-checking. InProceedings of the 30th ACM international con- ference on information & knowledge management, pages 1692–1702. Vincent A Traag, Ludo Waltman, and Nees Jan Van Eck. 2019. From louvain to leiden: guarantee- ing well-connected communities.Scientific reports, 9(1...

work page internal anchor Pith review arXiv 2019
[4]

Qwen3 Technical Report

Qwen3 technical report.arXiv preprint arXiv:2505.09388. Tian Zhang, Raghu Ramakrishnan, and Miron Livny

work page internal anchor Pith review Pith/arXiv arXiv
[5]

Birch: an efficient data clustering method for very large databases.ACM sigmod record, 25(2):103– 114. AMultiClaimTrain & Test Partition Topics Figure 7: 2D Projection of Two Topic Groups inMulti- Claim Figure 7 shows the 2D projection of claims be- longing to the two topic groups inMultiClaim. Al- though the groups were originally identified using the to...
[6]

for just 30 seconds

Table 11 shows an example fromMultiClaim- Test, whereBGE-M3fails to correctly group claims written in four different languages, resulting in dis- tinct clusters. In contrast,Claim2Vecsuccessfully corrects this split, likely due to the cross-lingual knowledge transfer learned during fine-tuning. (a) Topic Group 1 (b) Topic Group 2 Figure 8: Top 30 Frequent...