Morocco

The Uniform Information Density (UID) hypothesis posits that speakers tend to distribute information evenly across words within utterances, minimising surprisal variance. While this hypothesis was verified empirically, prior studies are limited exclusively to textвЂ“only inputs, abstracting away from the perceptual context. In this work, we present the first computational study of UID in grounded settings, estimating surprisal using multilingual visionвЂ“andвЂ“language models over imageвЂ“caption data in 30 languages and visual storytelling data in 14 languages, together spanning 11 families. We find that grounding on perception consistently smooths the distribution of information, lowering both global and local UID metrics across typologically diverse languages compared to text-only settings. In visual narratives, the combination of image and discourse contexts yields additive effects, with the strongest surprisal reductions at unit onsets. Grounded language exhibits greater information uniformity, supporting a context-sensitive formulation of UID. Hence, we take a first step towards modelling the temporal dynamics of information flow in ecologically plausible, multimodal language use.

EACL 2026

Is Information Density Uniform when Utterances are Grounded on Perception and Discourse?

multimodal grounding

uniform information density

surprisal

**Welcome to ACL-IJCNLP 2021!**

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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. 

Modern conversational AI systems require sophisticated Named Entity Recognition (NER) capabilities that can handle complex, contextual dialogue patterns. While Large Language Models (LLMs) excel at understanding conversational semantics, their inference latency and inability to efficiently incorporate emerging entities make them impractical for production deployment. Moreover, the scarcity of conversational NER data creates a critical bottleneck for developing effective models. We address these challenges through two main contributions. First, we introduce an automated pipeline for generating multilingual conversational NER datasets with minimal human validation, producing 4,082 English and 3,925 Spanish utterances. Second, we present a scalable framework that leverages LLMs as semantic filters combined with catalog-based entity grounding to label live traffic data, enabling knowledge distillation into faster, production-ready models. On internal conversational datasets, our teacher model demonstrates 39.55% relative F1-score improvement in English and 44.93% in Spanish compared to production systems. On public benchmarks, we achieve 97.12% F1-score on CoNLL-2003 and 83.09% on OntoNotes 5.0, outperforming prior state-of-the-art by 24.82 and 8.19 percentage points, respectively. Finally, student models distilled from our teacher approach achieve 13.84% relative improvement on English conversational data, bridging the gap between LLM capabilities and real-world deployment constraints.

When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World

As vision-language models (VLMs) are deployed globally, their ability to understand culturally situated knowledge becomes essential. Yet, existing evaluations largely assess static recall or isolated visual grounding, leaving unanswered whether VLMs possess robust and transferable cultural understanding. We introduce `BLEnD-Vis`, a multimodal, multicultural benchmark designed to evaluate the robustness of everyday cultural knowledge in VLMs across linguistic rephrasings and visual modalities. Building on the BLEnD dataset, `BLEnD-Vis` constructs 313 culturally grounded question templates spanning 16 regions and generates three aligned multiple-choice formats: (i) a text-only baseline querying from Region Entity, (ii) an inverted text-only variant (Entity Region), and (iii) a VQA-style version of (ii) with generated images. The resulting benchmark comprises 4,916 images and over 21,000 multiple-choice questions (MCQ) instances, validated through human annotation. `BLEnD-Vis` reveals significant fragility in current VLM cultural knowledge; models exhibit performance drops under linguistic rephrasing. While visual cues often aid performance, low cross-modal consistency highlights the challenges of robustly integrating textual and visual understanding, particularly in lower-resource regions. `BLEnD-Vis` thus provides a crucial testbed for systematically analysing cultural robustness and multimodal grounding, exposing limitations and guiding the development of more culturally competent VLMs. Code is available at https://github.com/Social-AI-Studio/BLEnD-Vis.

BLEnD-Vis: Benchmarking Multimodal Cultural Understanding in Vision Language Models

Large language models have simplified the production of personalized translations reflecting predefined stylistic constraints. However, these systems still struggle when stylistic requirements are implicitly represented by a set of examples, such as texts produced by a specific human translator. In this work, we explore various strategies for personalizing automatically generated translations when few examples are available, with a focus on the challenging domain of literary translation. We begin by determining the feasibility of the task and how style information is encoded within model representations. Then, we evaluate various prompting strategies and inference-time interventions for steering model generations towards a personalized style, with a particular focus on contrastive steering with sparse autoencoder (SAE) latents to identify salient personalization properties. We demonstrate that contrastive SAE steering yields robust style conditioning and translation quality, resulting in higher inference-time computational efficiency than prompting approaches. We further examine the impact of steering on model activations, finding that layers encoding personalization properties are impacted similarly by prompting and SAE steering, suggesting a similar mechanism at play.

Steering Large Language Models for Machine Translation Personalization

In-context knowledge editing (IKE) is a promising technique for updating Large Language Models (LLMs) with new information. However, IKE relies on lengthy, fact-specific demonstrations which are costly to create and consume significant context window space. In this paper, we introduce persuasion tokens (P-Tokens) -- special tokens trained to replicate the effect of IKE demonstrations, enabling efficient knowledge editing without requiring fact-specific demonstrations. We evaluate P-Tokens across two editing datasets and three LLMs, demonstrating performance comparable to, and often exceeding, IKE. We further find that editing performance is robust to distractors with some negative effects to neighboring facts, and that increasing the number of P-Tokens improves performance. Our work addresses key limitations of IKE and provides a more practical and scalable alternative for editing LLMs.

Persuasion Tokens for Editing Factual Knowledge in LLMs

Enterprise documents, such as forms and reports, embed critical information for downstream applications like data archiving, automated workflows, and analytics. Although generalist Vision Language Models (VLMs) perform well on established document understanding benchmarks, their ability to conduct holistic, fine-grained structured extraction across diverse document types and flexible schemas is not well studied. Existing Key Entity Extraction (KEE), Relation Extraction (RE), and Visual Question Answering (VQA) datasets are limited by narrow entity ontologies, simple queries, or homogeneous document types, often overlooking the need for adaptable and structured extraction. To address these gaps, we introduce ExStrucTiny, a new benchmark dataset for structured Information Extraction (IE) from document images, unifying aspects of KEE, RE, and VQA. Built through a novel pipeline combining manual and synthetic human-validated samples, ExStrucTiny covers more varied document types and extraction scenarios. We analyze open and closed VLMs on this benchmark, highlighting challenges such as schema adaptation, query under-specification, and answer localization. We hope our work provides a bedrock for improving generalist models for structured IE in documents.

ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images

Model steering, which involves intervening on hidden representations at inference time, has emerged as a lightweight alternative to finetuning for precisely controlling large language models. While steering efficacy has been widely studied, evaluations of whether interventions alter *only* the intended property remain limited, especially with respect to unintended changes in behaviors related to the target property. We call this notion specificity. We propose a framework that distinguishes three dimensions of specificity: general (preserving fluency and unrelated abilities), control (preserving related control properties), and robustness (preserving control properties under distribution shifts). We study two safety-critical use cases: steering models to reduce overrefusal and faithfulness hallucinations, and show that while steering achieves high efficacy and largely maintains general and control specificity, it consistently fails to preserve robustness specificity. In the case of overrefusal steering, for example, all steering methods reduce overrefusal without harming general abilities and refusal on harmful queries; however, they substantially increase vulnerability to jailbreaks. Our work provides the first systematic evaluation of specificity in model steering, showing that standard efficacy and specificity checks are insufficient, because without robustness evaluation, steering methods may appear reliable even when they compromise model safety.

Steering Safely or Off a Cliff? Rethinking Specificity and Robustness in Inference-Time Interventions

Large language models (LLMs) encode rich cultural knowledge learned from diverse web-scale data, offering an unprecedented opportunity to model cultural commonsense at scale. Yet this knowledge remains mostly implicit and unstructured, limiting its interpretability and use. We present an iterative, prompt-based framework for constructing a Cultural Commonsense Knowledge Graph (CCKG) that treats LLMs as cultural archives, systematically eliciting culture-specific entities, relations, and practices and composing them into multi-step inferential chains across languages. We evaluate CCKG on five countries with human judgments of cultural relevance, correctness, and path coherence. We find that the cultural knowledge graphs are better realized in English, even when the target culture is non-English (e.g., Chinese, Indonesian, Arabic), indicating uneven cultural encoding in current LLMs. Augmenting smaller LLMs with CCKG improves performance on cultural reasoning and story generation, with the largest gains from English chains. Our results show both the promise and limits of LLMs as cultural technologies and that chain-structured cultural knowledge is a practical substrate for culturally grounded NLP.

LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction

In this paper, we study the expansion of pluralia tantum, i.e., defective nouns which lack a singular form, like scissors. We base our work on an annotation framework specifically developed for the study of lexicalization of pluralia tantum, namely Lexicalization profiles. On a corresponding hand-annotated testset, we show that the OpenAI and DeepSeek models provide useful annotators for semantic, syntactic and sense categories, with accuracy ranging from 51% to 89%, averaged across all feature groups and languages. Next, we turn to a large-scale investigation of pluralia tantum. Using dictionaries, we extract candidate words for Italian, Russian and English and keep those for which the changing ratio of singular and plural form is evident in a corresponding reference corpus. We use an LLM to annotate each instance from the reference corpora according to the annotation framework. We show that the large amount of automatically annotated sentences for each feature can be used to perform in-depth linguistic analysis. Focusing on the correlation between an annotated feature and the grammatical form (singular vs. plural), patterns of morpho-semantic change are noted.

Elections go bananas: a first large-scale multilingual study of pluralia tantum using LLMs

In continual learning, our aim is to learn a new task without forgetting what was learned previously. In topic models, this translates to learning new topic models without forgetting previously learned topics. Previous work either considered Dynamic Topic Models (DTMs), which learn the evolution of topics based on the entire training corpus at once, or Online Topic Models, which are updated continuously based on new data but do not have long-term memory. To fill this gap, we propose the Continual Neural Topic Model (CoNTM), which continuously learns topic models at subsequent time steps without forgetting what was previously learned. This is achieved using a global prior distribution that is continuously updated. In our experiments, CoNTM consistently outperformed the dynamic topic model in terms of topic quality and predictive perplexity while being able to capture topic changes online. The analysis reveals that CoNTM can learn more diverse topics and better capture temporal changes than existing methods.

Continual Neural Topic Model

The medical adoption of NLP tools requires interpretability by end users, yet traditional XAI methods are misaligned with clinical reasoning and lack clinician input. We introduce CHiRPE (Clinical High-Risk Prediction with Explainability), an NLP pipeline that takes transcribed semi-structured clinical interviews to (i) predict psychosis risk and (ii) generate novel SHAP explanation formats co-developed with clinicians. Trained on 944 semi-structured interview transcripts across 24 international clinics of AMP-SCZ study, the CHiRPE pipeline integrates symptom-domain mapping, LLM summarisation and BERT classification, achieving above 90% accuracies across all three BERT variants, outperforming baseline models. Evaluation of explanation formats by twenty-eight clinical experts demonstrated strong preference for our novel concept-guided explanations, especially hybrid graph and text summary formats, over standard SHAP word-level bar plots and heat map visualisations. CHiRPE demonstrates that clinically-guided model development produces both accurate and interpretable results. Our next step is focused on real-world testing across our 24 international sites.

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When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World

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