Morocco

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&#39; 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.

EACL 2026

Contextual morphologically-guided tokenization for Latin encoder models

low-resources languages pos tagging

morphologically-rich languages pos tagging

subword representations

morphological segmentation

morphological analysis

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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.

poster

#### *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. 

Answering complex questions that require numerical reasoning over financial documents is challenging due to the diverse and scattered nature of relevant information. While large language models (LLMs) excel at financial reasoning, their enterprise deployment is often limited by cost and latency. Small language models (SLMs) present a cost-effective alternative but need to be fine-tuned with high-quality, domain-specific question-answer (QA) data. Acquiring such data requires manual expert annotation, presenting a bottleneck to the wider application of SLMs. This work introduces a modular, scalable end-to-end agentic pipeline that extracts and selects relevant content from unstructured financial documents and then generates QA pairs from the selected content for SLM fine-tuning. Compared to the same models trained on previous manually generated data for the task, one of the models trained on our pipeline-produced synthetic data achieved competitive in-distribution performance, and all tested models demonstrated superior generalization. The framework thus demonstrates considerable potential to accelerate the deployment of smaller, cost-effective models by reducing manual data creation efforts.

Synthesizing question answering data from financial documents: An End-to-End Multi-Agent Approach

Automated feature engineering (AutoFE) liberates data scientists from the burden of manual feature construction. The rich semantic information of datasets has been underutilized in many existing AutoFE works. We present PromptFE, a novel AutoFE framework that leverages large language models (LLMs) to automatically construct features in a compact string format and generate semantic explanations based on dataset descriptions. By learning the performance of constructed features in context, the LLM iteratively improves feature construction. We demonstrate through experiments on real-world datasets the superior performance of PromptFE over state-of-the-art methods. We verify the impact of dataset semantic information and provide comprehensive study on the LLM-based feature construction process.

PromptFE: Automated Feature Engineering by Prompting

Intrinsic evaluation metrics for conditional language models, such as perplexity or bits-per-character, are widely used in both mono- and multilingual settings. These metrics are rather straightforward to use and compare in monolingual setups, but rest on a number of assumptions in multilingual setups. One such assumption is that comparing the perplexity of CLMs on parallel sentences is indicative of their quality since the information content (here understood as the semantic meaning) is the same. However, the metrics are inherently measuring information content in the information-theoretic sense. We make this and other such assumptions explicit and discuss their implications. We perform experiments with six metrics on two multi-parallel corpora both with mono- and multilingual models. Ultimately, we find that current metrics are not universally comparable. We look at the form-meaning debate to provide some explanation for this.

Form and Meaning in Intrinsic Multilingual Evaluations

Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) is an increasingly explored approach for combining the reasoning capabilities of large language models with the structured evidence of knowledge graphs. However, current evaluation practices fall short: existing benchmarks often include questions that can be directly answered using existing triples in KG, making it unclear whether models perform reasoning or simply retrieve answers directly. Moreover, inconsistent evaluation metrics and lenient answer matching criteria further obscure meaningful comparisons. In this work, we introduce a general method for constructing benchmarks, together with an evaluation protocol, to systematically assess KG-RAG methods under knowledge incompleteness. Our empirical results show that current KG-RAG methods have limited reasoning ability under missing knowledge, often rely on internal memorization, and exhibit varying degrees of generalization depending on their design.

What Breaks Knowledge Graph based RAG? Empirical Insights into Reasoning under Incomplete Knowledge

Counterspeech generation has emerged as a promising approach to combat online hate speech, with recent work focusing on controlling attributes used in counterspeech, such as strategies or intents. While these attributes are often evaluated automatically using classifiers, a key goal of this evaluation is to compare the performance of different generation models. However, the validity of such evaluation results is questionable when the classifiers themselves have only modest performance. This paper examines the automatic evaluation of counterspeech attributes using a multi-attribute counterspeech dataset containing 2,728 samples. We investigate when automatic evaluation can be trusted for model comparison and address the limitations of current evaluation methodologies. We make concrete recommendations for how to perform classifier validation before model evaluation. Our classifier validation results demonstrate that even limited classifiers can produce trustworthy model rankings. Therefore, we argue that when comparing counterspeech generation models, a classifier's ability to rank generation models is a more direct measure of its practical utility than traditional classification metrics, e.g., accuracy and F1.

Validating Automatic Evaluation of Controllable Counterspeech Generation: Rankings Matter More Than Scores

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.

Statistical Foundations of DIME: Risk Estimation for Practical Index Selection

Analogical reasoning is an essential aspect of human cognition. In this paper, we summarize key theory about the processes underlying analogical reasoning from the cognitive science literature and relate it to current research in natural language processing. While these processes can be easily linked to concepts in NLP, they are generally not viewed through a cognitive lens. Furthermore, we show how these notions are relevant for several major challenges in NLP research, not directly related to analogy solving. This may guide researchers to better optimize relational understanding in text, as opposed to relying heavily on entity-level similarity.

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Synthesizing question answering data from financial documents: An End-to-End Multi-Agent Approach

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