Morocco

The principle of compositionality, which concerns the construction of meaning from constituent parts, is a longstanding topic in various disciplines, most commonly associated with formal semantics. In NLP, recent studies have focused on the compositional properties of text embedding models, particularly regarding their sensitivity to idiomatic expression, as idioms have traditionally been seen as non-compositional. In this paper, we argue that it is unclear how previous work relates to formal definitions of the principle. To address this limitation, we take a theoretically motivated approach based on definitions in formal semantics. We present 𝜀-compositionality, a continuous relaxation of compositionality derived from these definitions. We measure 𝜀-compositionality on a dataset containing both idiomatic and non-idiomatic sentences, providing a theoretically motivated assessment of sensitivity to idiomaticity. Our findings indicate that most text embedding models differentiate between idiomatic and non-idiomatic phrases, although to varying degrees.

EACL 2026

Measuring Idiomaticity in Text Embedding Models with 𝜀-compositionality

evaluation methodologies

multi-word expression

sentence embeddings

compositionality

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

technical paper

#### *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)* 

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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. 

The proliferation of multi-modal online advertisements necessitates robust content moderation to ensure user
safety, as offensive ad content can cause user distress and erode platform trust. This paper addresses the detection of content that becomes
offensive only when a user’s search query is paired with a specific ad, a context-dependent challenge that simple moderation often misses.
Key challenges include the nuanced, multi-modal nature of ads, severe data scarcity and class imbalance due to the rarity of offensive content, and the high cost of human labeling. To overcome these limitations, we introduce a novel, context-aware detection framework centered on a large-scale, Multi-modal Teacher-Student Knowledge Distillation architecture. A powerful Gemini encoder-only “teacher” model distills its knowledge into a lightweight student model suitable for low-latency deployment. We enhance robustness using a novel graph mining technique to find rare offensive examples for training. For evaluation, we developed a highly accurate Automated Evaluation Model (AEM)—a separate, larger Gemini model utilizing Chain-of-Thought (CoT) reasoning—to rigorously assess performance in a live A/B test. Our results demonstrate that the proposed framework reduces the serving of offensive query-ad pairs by more than 80% compared to the baseline,
while maintaining the efficiency required for real-time advertising systems that operate at a scale of over ≈ 100 billion query-ad pairs per day.

Enhancing User Safety: Context-Aware Detection of Offensive Query-Ad Pairs in Multimodal Search Advertising

Existing multimodal systems typically associate text and available images based on embedding similarity or simple co-location, but such approaches often fail to ensure that the linked image accurately depicts the specific product or component mentioned in a troubleshooting instruction. We introduce MIRAGE, a metadata-first paradigm that treats structured metadata, (not raw pixels), as a first-class modality for multimodal grounding. In MIRAGE, both text and images are projected through a shared semantic schema capturing product attributes, context, and visual aspects, enabling reasoning over interpretable attributes for troubleshooting rather than unstructured embeddings. MIRAGE comprises of three complementary modules: M-Link for schema-guided image–text linking, M-Gen for metadata-conditioned multimodal generation, and M-Eval for consistency evaluation in the same structured space. Experiments on large-scale enterprise e-commerce troubleshooting data across 10 product types on 100K text chunks and 35K images show that metadata-centric grounding achieves over 40% higher linking coverage of high-quality visual content and over 45% in linking and response quality than embedding-based baselines. MIRAGE demonstrates the potential of structured metadata in enabling scalable, fine-grained grounding in multimodal troubleshooting systems.

MIRAGE: Metadata-guided Image Retrieval and Answer Generation for E-commerce Troubleshooting

Merging fine-tuned models is a promising alternative to costly multi-task training, but task interference remains a challenge, especially as the number of tasks grows. We present DivMerge, a reference-free method that merges models trained on different tasks by minimizing Jensen-Shannon divergence between their outputs and those of the merged model, automatically balancing task importance. While the method exhibits strong theoretical properties, experiments on classification and generative tasks with autoregressive models show that DivMerge consistently outperforms prior work, and remains robust when scaling to more tasks.

DivMerge: A divergence-based model merging method for multi-tasking

We investigate the automated evaluation of English language learner answers to writing prompts featuring picture stories. Automatic evaluation of such tasks is usually limited to language proficiency only, neglecting the context of the picture. Instead, our analysis focuses on task adherence, which for example allows detection of off-topic answers. Since there is a lack of suitable training and evaluation data, our first step is to build the PictureStories dataset. To this end, we develop a marking rubric that covers task adherence with respect to form and content. Six annotators mark 713 learner answers written in response to one of five picture stories. Having assembled the dataset, we then explore to what extent task adherence can be predicted automatically. Our experiments assume a scenario where no or just a few labelled answers are available for the picture story which is being marked. For content-focused criteria, few-shot prompting Qwen emerges as the best-performing method. We examine the trade-off between including the story image vs. example answers in the prompt and find that examples suffice in many cases. For form-focused criteria, we find that it is beneficial to finetune models across prompts. For some LLMs, few-shot prompting results may look promising on the surface, but we demonstrate that a much simpler method can go just as far when shown the same examples.

PictureStories: Predicting the Task Adherence of Language Learner Answers to a Picture Story-Based Writing Task

In this work, we focus on discovery of legal factors for a specific case type under consideration (e.g., vehicle insurance disputes). We refer to these legal factors more explicitly as "Argumentation and Judgement Factors" (AJFs). AJFs encode specific legal knowledge that is important for legal argumentation and judicial decision making. We propose a multi-step approach for discovering a list of AJFs for a given case type using a set of relevant legal documents (e.g., past judgements, relevant acts) and Symbolic Knowledge Distillation (SKD) from a Large Language Model (LLM). We propose a novel geneRatE-CRitic-reviEW (RECREW) prompting strategy for effective SKD. We construct and evaluate the discovered list of AJFs on two different types of cases (auto-insurance and life insurance) and show their utility in a dispute resolution application.

Argumentation and Judgement Factors: LLM-based Discovery and Application in Insurance Disputes

Idioms present a unique challenge for language models due to their non-compositional figurative interpretations, which often strongly diverge from the idiom's literal interpretation. In this paper, we employ causal tracing to systematically analyze how pretrained causal transformers deal with this ambiguity. We localize three mechanisms: (i) Early sublayers and specific attention heads retrieve figurative interpretation, while suppressing literal interpretation. (ii) When disambiguating context precedes the idiom, the model leverages it from the earliest layer and later layers refine the interpretation if the context conflicts with the retrieved interpretation. (iii) Then, selective, competing pathways carry both interpretations: an intermediate pathway that prioritizes the figurative interpretation and a parallel direct route that favors literal interpretation, ensuring that both readings remain available. Our findings provide mechanistic evidence for idiom comprehension in autoregressive transformers.

Tug-of-war between idioms' figurative and literal interpretations in LLMs

With the accelerated scaling in large language models' (LLMs) sizes, comes the prohibitive surge in computation budget allotted for model's alignment and fine-tuning for downstream tasks. And while factual knowledge has been shown to be learned earlier during pre-training, a model still requires further fine-tuning to adhere to task requirements and stylistic preferences. We propose a novel approach to circumvent costly large model fine-tuning via test-time alignment, i.e. using guidance from a small aligned model. Our approach is based on a new formulation of token-specific cascading as a 0-1 knapsack problem. In this setting, we derive primal and dual approximations of the optimal deferral decision. We further demonstrate the advantages of our method experimentally both in task performance and speculative decoding speed.

KAD: A Framework for Proxy-based Test-time Alignment with Knapsack Approximation Deferral

The basic underlying assumption of machine learning (ML) models is that the training and test data are sampled from the same distribution. However, in daily practice, this assumption is often broken, i.e. the distribution of the test data changes over time, which hinders the application of conventional ML models. One domain where the distribution shift naturally occurs is text classification, since people always find new topics to discuss. To this end, we survey research articles studying open-set text classification and related tasks. We divide the methods in this area based on the constraints that define the kind of distribution shift and the corresponding problem formulation, i.e.~learning with the Universum, zero-shot learning, and open-set learning. We next discuss the predominant mitigation approaches for each problem setup. We further identify several future work directions, aiming to push the boundaries beyond the state of the art. Finally, we explain how continual learning can solve many of the issues caused by the shifting class distribution.

Text Classification Under Class Distribution Shift: A Survey

User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communication, forms the foundation of social interaction and behavior. Consequently, simulating conversational behavior has become a key area of study. Recent advancements in large language models (LLMs) have significantly catalyzed progress in this domain by enabling high-fidelity generation of synthetic user conversation. In this paper, we survey recent advancements in LLM-based conversational user simulation. We introduce a novel taxonomy covering user granularity and simulation objectives. Additionally, we systematically analyze core techniques and evaluation methodologies. We aim to keep the research community informed of the latest advancements in conversational user simulation and to further facilitate future research by identifying open challenges and organizing existing work under a unified framework.

A Survey on LLM-based Conversational User Simulation

Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance by dynamically assigning queries to the most appropriate model based on query complexity. Despite recent advances showing that preference-data-based routers can outperform traditional methods, current evaluation benchmarks remain limitedвЂ”they largely focus on general model capabilities while overlooking task-specific behaviors and critical concerns such as privacy, safety, and potential backdoor vulnerabilities introduced through preference data. In response, we propose the DSC benchmark: Diverse, simple, and categorized, an evaluation framework that categorizes router performance across a broad spectrum of query typesвЂ”including coding, translation, mathematics, human instructions, general knowledge, and LLM jailbreakingвЂ”and integrates privacy and safety assessments to reveal hidden risks. Our experiments on three preference-based routers and two commercial counterparts demonstrate that while these systems improve efficiency, they often make suboptimal, category-driven decisions; for instance, a BERT-based router directs all coding and mathematics queries to the most powerful LLMвЂ”even when simpler models would sufficeвЂ”while routing jailbreaking attempts to weaker models, thereby elevating safety risks.

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