Morocco

Entity representation learning on hypergraphs is hindered by degree bias, where nodes with sparse connections suffer from limited structural information for aggregation. Prevailing “attribute-as-feature“ approaches, which treat rich textual attributes (e.g., titles, abstracts, keywords) merely as node features, fail to address this structurally rooted problem as they do not create new aggregation pathways. To overcome this limitation, we propose a novel “attribute-as-structure“ approach specifically designed for heterogeneous hypergraphs. Our approach integrates attributes directly into the hypergraph topology as distinct node types, creating new structural pathways to enrich sparsely connected entities while preserving semantic distinctiveness within complex many-to-many hyperedge interactions. We introduce an entity-attribute aware learning framework featuring two key innovations: (1) a specialized heterogeneous hypergraph encoder with dual attention mechanisms—self-attention for entity-entity relationships and cross-type attention for entity-attribute relevance, and (2) Attribute-Attentive Contrastive Learning (AACL), a novel objective that dynamically weighs attribute importance while explicitly aligning entity representations with their structural attributes. Experiments on multiple hypergraph datasets demonstrate consistent improvements in node classification performance, with particularly significant gains for structurally sparse nodes, demonstrating the effectiveness of our approach for degree bias mitigation.

EACL 2026

Mitigating Degree Bias in Hypergraphs via Attribute-as-Structure Approach

degree bias

hypergraph representation learning

contrastive learning

## Welcome to EMNLP 2024! 
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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.
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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. 

Test-time scaling (TTS) has enhanced the performance of Reasoning Models (RMs) on various tasks such as math and coding, yet its efficacy in machine translation (MT) remains underexplored. This paper investigates whether increased inference-time computation improves translation quality. We evaluate 12 RMs across a diverse suite of MT benchmarks spanning multiple domains, examining three scenarios: direct translation, forced-reasoning extrapolation, and post-editing. Our findings show that for general-purpose RMs, TTS provides limited and inconsistent benefits for direct translation, with performance quickly plateauing. However, the effectiveness of TTS is unlocked by domain-specific fine-tuning, which aligns a model's reasoning process with task requirements, leading to consistent improvements up to an optimal, self-determined reasoning depth. We also find that forcing a model to reason beyond its natural stopping point consistently degrades translation quality. In contrast, TTS proves highly effective in a post-editing context, reliably turning self-correction into a beneficial process. These results indicate that the value of inference-time computation in MT lies not in enhancing single-pass translation with general models, but in targeted applications like multi-step, self-correction workflows and in conjunction with task-specialized models.

Test-Time Scaling of Reasoning Models for Machine Translation

Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the complexity of real-world RAG systems, there are many potential causes for erroneous outputs. Understanding the range of errors that can occur in practice is crucial for robust deployment. We present a new taxonomy of the error types that can occur in realistic RAG systems, examples of each, and practical advice for addressing them. Additionally, we curate a dataset of erroneous RAG responses annotated by error types. We then propose an auto-evaluation method aligned with our taxonomy that can be used in practice to track and address errors during development. Code and data have been released publicly.

Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems

Zero-shot dense retrieval is a challenging setting where a document corpus is provided without relevant queries, necessitating a reliance on pretrained dense retrievers (DRs). However, since these DRs are not trained on the target corpus, they struggle to represent semantic differences between similar documents. To address this failing, we introduce a training-free representation sharpening framework that augments a document's representation with information that helps differentiate it from similar documents in the corpus. On over twenty datasets spanning multiple languages, the representation sharpening framework proves consistently superior to traditional retrieval, setting a new state-of-the-art on the BRIGHT benchmark. We show that representation sharpening is compatible with prior approaches to zero-shot dense retrieval and consistently improves their performance. Finally, we address the performance-cost tradeoff presented by our framework and devise an indexing-time approximation that preserves the majority of our performance gains over traditional retrieval, yet suffers no additional inference-time cost.

A Representation Sharpening Framework for Zero Shot Dense Retrieval

Large language models (LLMs) have emerged as strong contenders in machine translation. Yet, they still struggle to adequately handle discourse phenomena, such as pronoun resolution and lexical cohesion at the document level. In this study, we thoroughly investigate the discourse phenomena performance of LLMs in context-aware translation. We demonstrate that discourse knowledge is encoded within LLMs and propose the use of quality-aware decoding (QAD) to effectively extract this knowledge, showcasing its superiority over other decoding approaches through comprehensive analysis. Furthermore, we illustrate that QAD enhances the semantic richness of translations and aligns them more closely with human preferences.

Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding

The rapid evolution of large language models (LLMs) and the real world has outpaced the static nature of widely used evaluation benchmarks, raising concerns about their reliability for evaluating LLM factuality. While substantial works continue to rely on the popular but old benchmarks, their temporal misalignment with real-world facts and modern LLMs, and their effects on LLM factuality evaluation remain underexplored. Therefore, in this work, we present a systematic investigation of this issue by examining five popular factuality benchmarks and eight LLMs released across different years. An up-to-date fact retrieval pipeline and three metrics are tailored to quantify benchmark aging and its impact on LLM factuality evaluation. Experimental results and analysis illustrate that a considerable portion of samples in the widely used factuality benchmarks are outdated, leading to unreliable assessments of LLM factuality. We hope our work can provide a testbed to assess the reliability of a benchmark for LLM factuality evaluation and inspire more research on the benchmark aging issue.

When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation

Tokenization is a critical component of language model pretraining, yet standard tokenization methods often prioritize information-theoretical goals like high compression and low fertility rather than linguistic goals like morphological alignment. In fact, they have been shown to be suboptimal for morphologically rich languages, where tokenization quality directly impacts downstream performance. In this work, we investigate morphologically-aware tokenization for Latin, a morphologically rich language that is medium-resource in terms of pretraining data, but high-resource in terms of curated lexical resources -- a distinction that is often overlooked but critical in discussions of low-resource language modeling. We find that morphologically-guided tokenization improves overall performance on four downstream tasks. Performance gains are most pronounced for out of domain texts, highlighting our models' improved generalization ability. Our findings demonstrate the utility of linguistic resources to improve language modeling for morphologically complex languages. For low-resource languages that lack large-scale pretraining data, the development and incorporation of linguistic resources can serve as a feasible alternative to improve LM performance.

Contextual morphologically-guided tokenization for Latin encoder models

Large language models (LLMs) excel in English but still struggle with complex reasoning in many low-resource languages (LRLs). Existing methods align LLMs with multilingual encoders, such as LangBridge and MindMerger, raising the accuracy for mid and high-resource languages, yet large performance gap remains for LRLs. We present MERLIN, a model-stacking framework that iteratively refines in 2-stages based on a curriculum strategy (from general to specific where general is bilingual bitext and specific is task-specific data) and adapts only a small set of DoRA weights. On the AfriMGSM benchmark MERLIN improves exact-match accuracy by +12.9 pp over MindMerger and outperforms GPT-4o-mini by 15.2 pp. It also yields consistent gains on MGSM and MSVAMP (+0.9 and +2.8 pp), demonstrating effectiveness across both low and high-resource settings.

MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning

We present a comprehensive evaluation of large language models’ (LLMs) ability to process culturally grounded language, specifically to understand and pragmatically use figurative expressions that encode local knowledge and social nuance. Using figurative language as a proxy for cultural nuance and local knowledge, we design evaluation tasks for contextual understanding, pragmatic use, and connotation interpretation across Arabic and English. We evaluate 22 open- and closed-source LLMs on Egyptian Arabic idioms, multidialectal Arabic proverbs, and English proverbs. Results show a consistent hierarchy: accuracy on Arabic proverbs is 4.29% lower than on English proverbs, and performance on Egyptian idioms is 10.28% lower than on Arabic proverbs. On the pragmatic use task, accuracy drops by 14.07% relative to understanding, though providing idioms’ contextual sentences improves accuracy by 10.66%. Models also struggle with connotative meaning, reaching at most 85.58% agreement with human annotators on idioms with full inter-annotator agreement. Figurative language thus serves as an effective diagnostic for cultural reasoning, revealing that while LLMs often interpret figurative meaning, they still face major challenges in using it appropriately. To support future research, we release Kinayat, the first dataset of Egyptian Arabic idioms designed for both figurative understanding and pragmatic use evaluation.

Beyond Understanding: Evaluating the Pragmatic Gap in LLMs' Cultural Processing of Figurative Language

Intrinsic dimension (ID) is an important tool in modern LLM analysis, informing studies of training dynamics, scaling behavior, and dataset structure, yet its textual determinants remain underexplored. We provide the first comprehensive study grounding ID in interpretable text properties through cross-encoder analysis, linguistic features, and sparse autoencoders (SAEs). In this work, we establish three key findings. First, ID is complementary to entropy-based metrics: after controlling for length, the two are uncorrelated, with ID capturing geometric complexity orthogonal to prediction quality. Second, ID exhibits robust genre stratification: scientific prose shows low ID (8), encyclopedic content medium ID (9), and creative/opinion writing high ID (10.5) across all models tested. This reveals that contemporary LLMs find scientific text "representationally simple" while fiction requires additional degrees of freedom. Third, using SAEs, we identify causal features: scientific signals (formal tone, report templates, statistics) reduce ID; humanized signals (personalization, emotion, narrative) increase it. Steering experiments confirm these effects are causal. Thus, for contemporary models, scientific writing appears comparatively "easy", whereas fiction, opinion, and affect add representational degrees of freedom. Our multi-faceted analysis provides practical guidance for the proper use of ID and the sound interpretation of ID-based results.

Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story

High-dimensional dense embeddings have become central to modern Information Retrieval, but many dimensions are noisy or redundant. Recently proposed DIME (Dimension IMportance Estimation), provides query-dependent scores to identify informative components of embeddings. DIME relies on a costly grid search to select a priori a dimensionality for all the query corpus’s embeddings. Our work provides a statistically grounded criterion that directly identifies the optimal set of dimensions for each query at inference time. Experiments confirm that this approach improves retrieval effectiveness and reduces embedding size by an average ~50 % of across different models and datasets at inference time.

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Test-Time Scaling of Reasoning Models for Machine Translation

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