Morocco

This paper describes VILLAIN, a multimodal fact-checking system that verifies image-text claims through prompt-based multi-agent collaboration. For the AVerImaTeC shared task, VILLAIN employs vision-language model agents across multiple stages of fact-checking. Textual and visual evidence is retrieved from the knowledge store enriched through additional web collection. To identify key information and address inconsistencies among evidence items, modality-specific and cross-modal agents generate analysis reports. In the subsequent stage, question-answer pairs are produced based on these reports. Finally, the Verdict Prediction agent produces the verification outcome based on the image-text claim and the generated question-answer pairs. Our system ranked first on the leaderboard across all evaluation metrics. The source code is publicly available at https://github.com/ssu-humane/VILLAIN.

EACL 2026

VILLAIN at AVerImaTeC: Verifying Image–Text Claims via Multi-Agent Collaboration

image-text claim verification

multi-agent collaboration

multimodal fact-checking

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

workshop paper

In this paper, we present our 3rd place system in the AVerImaTeC shared task, which combines our last year's retrieval-augmented generation (RAG) pipeline with a reverse image search (RIS) module. Despite its simplicity, our system delivers competitive performance with a single multimodal LLM call per fact-check at just $0.013 on average using GPT5.1 via OpenAI Batch API. Our system is also easy to reproduce and tweak, consisting of only three decoupled modules — a textual retrieval module based on similarity search, an image retrieval module based on API-accessed RIS, and a generation module using GPT5.1 — which is why we suggest it as an accesible starting point for further experimentation. We publish its code and prompts, as well as our vector stores and insights into the scheme's running costs and directions for further improvement.

AIC CTU@AVerImaTeC: dual-retriever RAG for image-text fact checking

This paper presents an efficiency-aware pipeline for automated fact-checking of real-world image–text claims that treats multimodality as a controllable design variable rather than a property that must be uniformly propagated through every stage of the system. The approach decomposes claims into verification questions, assigns each to text- or image-related types, and applies modality-aware retrieval strategies, while ultimately relying on text-only evidence for verdict prediction and justification generation. Evaluated on the AVerImaTeC dataset within the FEVER-9 shared task, the system achieves competitive question, evidence, verdict, and justification scores and ranks fourth overall, outperforming the official baseline on evidence recall, verdict accuracy, and justification quality despite not using visual evidence during retrieval. These results demonstrate that strong performance on multimodal fact-checking can be achieved by selectively controlling where visual information influences retrieval and reasoning, rather than performing full multimodal fusion at every stage of the pipeline

Selective Multimodal Retrieval for Automated Verification of Image–Text Claims

Optimizer choice is a central hyperparameter in fine-tuning transformer models, yet its impact remains under-studied for Arabic-script social media classification under class imbalance. We compare Adam, AdamW, and SGD for fine-tuning QARiB on two Arabic offensive-language benchmarks, OffensEval20 and MPOLD, using a controlled grid over learning rate, weight decay, and warmup, and report test-set performance as mean (std) over three random seeds.

Minority-class discrimination is evaluated using macro-F1 and AUC-PROFF, while calibration is assessed via expected calibration error (ECE) and reliability diagrams. Across both datasets, AdamW and Adam are consistently strong and closely matched when properly tuned, whereas SGD substantially underperforms under the same tuning budget and exhibits higher seed sensitivity.

We observe non-trivial miscalibration across optimizers; post-hoc temperature scaling offers a low-cost adjustment, yielding modest, dataset-dependent changes in ECE while preserving ranking-based discrimination. In practice, for Arabic offensive-language detection under imbalance, we recommend starting from a tuned AdamW or Adam baseline; when calibrated probabilities are required for thresholding or triage, temperature scaling can be applied. We will release a reproducible pipeline to support further evaluation of optimizer–calibration trade-offs in Arabic-script safety tasks.

Optimizer Choice and Calibration for QARiB on Arabic-Script Social Media Offensive Language Detection

Despite the advances in neural text to speech
(TTS), many Arabic dialectal varieties remain
marginally addressed, with most resources con-
centrated on Modern Spoken Arabic (MSA)
and Gulf dialects, leaving Egyptian Arabic—
the most widely understood Arabic dialect—
severely under-resourced. We address this
gap by introducing NileTTS: 38 hours of tran-
scribed speech from two speakers across di-
verse domains including medical, sales, and
general conversations. We construct this
dataset using a novel synthetic pipeline: large
language models (LLM) generate Egyptian
Arabic content, which is then converted to natu-
ral speech using audio synthesis tools, followed
by automatic transcription and speaker diariza-
tion with manual quality verification. We fine-
tune XTTS v2, a state-of-the-art multilingual
TTS model, on our dataset and evaluate against
the baseline model trained on other Arabic
dialects. Our contributions include: (1) the
first publicly available Egyptian Arabic TTS
dataset, (2) a reproducible synthetic data gen-
eration pipeline for dialectal TTS, and (3) an
open-source fine-tuned model. All resources
are released to advance Egyptian Arabic speech
synthesis research.

LLM-to-Speech: A Synthetic Data Pipeline for Training Dialectal Text-to-Speech Models

Kazakh is a Turkic language using the Arabic, Cyrillic, and Latin scripts, making it unique in terms of optical character recognition (OCR). Work on OCR for low-resource Kazakh scripts is very scarce, and no OCR benchmarks or images exist for the Arabic and Latin scripts. We construct a synthetic OCR dataset of 7,219 images for all three scripts with font, color, and noise variations to imitate real OCR tasks. We evaluated three multimodal large language models (MLLMs) on a subset of the benchmark for OCR and language identification: Gemma-3-12B-it, Qwen2.5-VL-7B-Instruct, and Llama-3.2-11B-Vision-Instruct. All models are unsuccessful with Latin and Arabic script OCR, and fail to recognize the Arabic script as Kazakh text, misclassifying it as Arabic, Farsi, and Kurdish. These findings show significant gaps in current MLLM capabilities to process low-resource Abjad-based scripts and demonstrate the need for inclusive models and benchmarks supporting low-resource scripts and languages.

KazakhOCR: A Synthetic Benchmark for Evaluating Multimodal Models in Low-Resource Kazakh Script OCR

This study addresses the task of automatic transliteration from Tajik (Cyrillic script) to Persian (Perso-Arabic script). We present a curated, lexicographically verified parallel corpus of 52,152 Tajik–Persian words and short phrases, compiled from authoritative printed dictionaries, encyclopedic sources, and manually verified online resources. To our knowledge, this is the largest high-quality word-level corpus available for transliteration research.

Using this corpus, we train a character-level sequence-to-sequence Transformer model and evaluate its performance with Character Error Rate (CER) and exact-match accuracy. The best model achieves a CER of 0.3216 and exact-match accuracy of 0.3133, outperforming both dictionary-based rule-oriented and recurrent neural models. We provide a detailed description of the data collection and preprocessing, model architecture, and experimental protocol, and we conduct a qualitative analysis of typical transliteration errors, including phonetic ambiguity, orthographic variations, and rare forms.

All preprocessing scripts, deterministic splits into training, validation, and test sets, and training configurations are released to ensure reproducibility and to support further research on the Tajik language and related Persian dialects. The corpus provides a reliable foundation for character-level transliteration and contributes to the development of applications in cross-script NLP, digital lexicography, and language learning.

Character-Level Transformer for Tajik–Persian Transliteration with a Parallel Lexical Corpus

Named Entity Recognition (NER) models trained on clean text often fail on real-world data containing orthographic noise. Work on NER for Persian is emerging, but it has not yet explored the orthographic robustness of models to perturbations often exhibited in user-generated content. We evaluate ParsBERT, ParsBERT v2.0, and BertNER on a subset of Persian-NER-Dataset-500k after applying eleven different perturbations, including simulated typos, code-switching, and segmentation errors. All models were competitive with each other, but ParsBERT v2.0 consistently displayed the best robustness to perturbations. Code-switching, typos, similar character swaps, segmentation errors, and noisy text all decreased F1 scores, while Latinized numbers increased F1 scores in ParsBERT. Removing diacritics, zero-width non-joiners, and normalizing Yeh/Kaf all did not have an effect on F1. These findings suggest that Persian NER models require improvement for performance on noisy text, and that the Perso-Arabic script introduces unique factors into NER not present in many high-resource languages, such as code-switching and Eastern Arabic numerals. This work creates a foundation for the development of robust Persian NER models and highlights the necessity of evaluating low-resource NER models under challenging and realistic conditions.

Orthographic Robustness of Persian Named Entity Recognition Models

Hausa Ajami (Hausa written in Arabic script) remains severely under-resourced for computational morphology. We present AjamiMorph, a zero-annotation framework that discovers morphemes through consensus among three unsupervised methods, viz., Byte Pair Encoding (BPE), transition-based boundary detection using Pointwise Mutual Information (PMI), and computational-linguistics-based Distributional Affix Mining (DAM).

Using a Hausa Ajami Bible corpus consisting of 637,414 tokens, AjamiMorph identifies 1,611 high-confidence morphemes, achieving 99.9% coverage. The inventory exhibits a linguistically realistic distribution (66.0% stems, 22.6% suffixes, 11.4% prefixes) and recovers 77.8% of known Hausa affixes. We also report negative results for script-driven Arabic assumptions and LLM-based approaches.

This work provides the first unsupervised morpheme inventory for Hausa Ajami and demonstrates consensus as a robust strategy for zero-resource morphology.

AjamiMorph: Zero-Annotation Morphological Discovery for Hausa Ajami via Multi-Method Consensus

As reported, approximately 6 million people in Iraq and Iran speak Sorani Kurdish, which exhibits substantial regional variation but lacks computational resources for dialect identification. We present the first fine-grained sub-dialect classification system for six Sorani varieties, viz., Sulaymaniyah, Erbil, Iranian Sorani, Ardalani, Babani, and Mukriani.

This investigation combines cross-lingual contextual embeddings (XLM-RoBERTa) with morphological features derived from explicit linguistic rules, including 24 patterns capturing verb prefixes, pronominal clitics, and definite markers. The proposed morphology-augmented XLM-R model is trained on 16,409 sentences without manual annotation and achieves 91.91% accuracy, outperforming pure transformer models (91.79%) and traditional machine-learning baselines (SVM: 86.41%).

Key ablation studies reveal that morphological features act as effective regularizers for geographically proximate dialects.

Morphological Feature Extraction for Fine-Grained Sorani Kurdish Dialect Identification: A Hybrid Transformer-Linguistic Approach

Automatic classification of literary text by historical era can support literary analysis and reveal stylistic evolution. We study this problem for Urdu poetry across three eras, classical, modern, and contemporary. We introduce a new dataset of 10,026 four-line Urdu poetry segments collected from online archives (Rekhta and UrduPoint) and labeled by era. To handle Urdu's script and orthographic variability, we apply standard preprocessing, including Unicode normalization and removal of diacritics and non-Urdu characters. We benchmark a range of approaches, from traditional machine learning classifiers to deep learning models, including four fine-tuned Urdu BERT-style transformers. To assess generalization, we evaluate under two regimes: (i) a standard stratified random split and (ii) a stricter author-disjoint split that ensures poets do not overlap between training and test sets. On the random split, the best traditional models achieve about 70–73% accuracy, suggesting era-related stylistic cues are learnable. However, performance drops to roughly 58–60% under the author-disjoint split, highlighting the difficulty in generalizing across unseen poets and the possibility of overestimating performance via author-specific leakage. Notably, fine-tuned transformers do not surpass simpler TF–IDF-based baselines, indicating that era cues may be subtle and data-limitation for complex models.

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