Morocco

Despite extensive research in intent classification, most task-oriented dialogue systems still rigidly assign intents to user utterances without addressing ambiguity, often leading to misrouted requests, irrelevant responses, and user frustration. Proprietary large-language models (LLMs) can generate effective clarifying questions but are too costly for large-scale deployment. Smaller open-source LLMs are more economical, but struggle to ask appropriate clarifying questions. This paper introduces a domain-agnostic framework that equips lightweight, production-ready open-source LLMs with the ability to perform intent classification alongside precise ambiguity resolution via clarifying questions. We validate our framework on both proprietary and public intent classification datasets, demonstrating its ability to perform intent classification as well as generate clarification questions in case of ambiguity. To compare models, those trained with our framework and external baselines, we also propose an evaluation methodology that jointly assesses the accuracy of intent classification and the timing and quality of clarifying questions. Our instruction-tuned models achieve performance comparable to leading proprietary LLMs while offering an 8X reduction in inference cost, enabling broader, cost-efficient deployment. When deployed in the customer-care system of an e-commerce enterprise, our model reduced the misrouting rate by 8%, resulting in a significant improvement in automation rates, which potentially translates in dollar savings by reducing escalations to human agents.

EACL 2026

Scaling Intent Understanding: A Framework for Classification with Clarification using Lightweight LLMs

ambiguous query resolution

lightweight llms

intent classification with clarification

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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. 

Traditional customer support systems, such as Interactive Voice Response (IVR), rely on rigid scripts and lack the flexibility required for handling complex, policy-driven tasks. While large language model (LLM) agents offer a promising alternative, evaluating their ability to act in accordance with business rules and real-world support workflows remains an open challenge. Existing benchmarks primarily focus on tool usage or task completion, overlooking an agent's capacity to adhere to multi-step policies, navigate task dependencies, and remain robust to unpredictable user or environment behavior. In this work, we introduce JourneyBench, a benchmark designed to assess policy-aware agents in customer support. JourneyBench leverages graph representations to generate diverse, realistic support scenarios and proposes the User Journey Coverage Score, a novel metric to measure policy adherence. We evaluate multiple state-of-the-art LLMs using two agent designs: a Static-Prompt Agent (SPA) and a Dynamic-Prompt Agent (DPA) that explicitly models policy control. Across 703 conversations in three domains, we show that DPA significantly boosts policy adherence, even allowing smaller models like GPT-4o-mini to outperform more capable ones like GPT-4o. Our findings demonstrate the importance of structured orchestration and establish JourneyBench as a critical resource to advance AI-driven customer support beyond IVR-era limitations.

Beyond IVR: Benchmarking Customer Support LLM Agents for Business-Adherence

OCR for Bangla scripts remains a challenging problem, with existing solutions limited to single-domain processing. Current approaches lack a unified vision encoder that can understand diverse Bangla script variations, hindering practical deployment. We present BornoDrishti, the first unified OCR system based on the vision transformer that accurately recognizes both printed and handwritten Bangla scripts within a single model. Our approach introduces a novel domain objective that enables the model to learn domain-invariant representations while preserving script-specific features, eliminating the need for separate domain experts. BornoDrishti achieves competitive accuracy across both domains, setting state-of-the-art performance for printed scripts and demonstrating that a single unified model can match or exceed specialized uni-domain systems. We evaluate our model against state-of-the-art domain-specific and cross-domain OCR systems. This work establishes a foundation for advancing practical applications by using a unified multi-domain OCR system for complex Bangla scripts.

BornoDrishti: Leveraging Vision Encoders and Domain-Adaptive Learning for Bangla OCR on Diverse Documents

Opinion mining from real-world student feedback presents significant practical challenges, such as handling linguistic noise (slang, teencode) and the need for scalable and maintainable systems, which are often overlooked in academic research. This paper introduces EduPulse, a practical opinion mining system designed specifically to analyze student feedback in Vietnamese. Our application performs four opinion analysis tasks, including Sentiment Classification, Category-based Sentiment Classification, Suggestion Detection, and Opinion Summarization. We design the hybrid architecture that strategically balances performance, cost, and maintainability. This architecture leverages the robustness of Large Language Models (LLMs) for complex, noise-sensitive tasks as sentiment classification and suggestion detection, while employing a specialized, lightweight neural model for high-throughput, low-cost solutions. Our experiments show that applying the LLM-based approach achieves high robustness, justifying its operational cost by eliminating expensive retraining cycles. Furthermore, we demonstrate that our collaborative modular architecture significantly improves task performance (+7.6\%) compared to traditional approaches, offering a practical design for industry-focused Natural Language Processing applications.

EduPulse: A Practical LLM-Enhanced Opinion Mining System for Vietnamese Student Feedback in Educational Platforms

Large language models (LLMs) have achieved remarkable progress, yet their internal mechanisms remain largely opaque, posing a significant challenge to their safe and reliable deployment. Sparse autoencoders (SAEs) have emerged as a promising tool for decomposing LLM representations into more interpretable features, but explaining the features captured by SAEs remains a challenging task. In this work, we propose SAGE (SAE Agentic Explainer), an agent-based framework that recasts feature interpretation from a passive, single-pass generation task into an active, explanation-driven process. SAGE implements a rigorous methodology by systematically formulating multiple explanations for each feature, designing targeted experiments to test them, and iteratively refining explanations based on empirical activation feedback. Experiments on features from SAEs of diverse language models demonstrate that SAGE produces explanations with significantly higher generative and predictive accuracy compared to state-of-the-art baselines.

SAGE: An Agentic Explainer Framework for Interpreting SAE Features in Language Models

Social media platforms have become primary sources for news consumption due to their real-time and interactive nature, yet they have also facilitated the widespread proliferation of misinformation, negatively impacting public health, social cohesion, and market stability. While professional fact-checking is essential for debunking rumors, the process is time-consuming, necessitating automation to effectively combat fake news. Existing approaches, such as extractive methods, often lack coherence and context, whereas abstractive methods leveraging large language models (LLMs) can generate more readable and informative debunking passages. However, readability alone is insufficient for effective misinformation correction; user acceptance is critical. Recent advancements in LLMs offer new opportunities for personalized debunking, as these models can generate context-sensitive responses and adapt to user profiles. Building on this, we propose the MUti-round Refinement and Simulated fEedback-enhanced framework (MURSE), which generates Chinese user-specific debunking passages by iteratively refining outputs based on simulated user feedback. Specifically, MURSE-generated user-specific debunking passages were preferred twice as often as general debunking passages in most cases, highlighting its potential to improve misinformation correction and foster positive dissemination chains.

Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework

The labor market is experiencing rapid and continual shifts in required skills and competencies, driven by technological advancement and evolving industry structures. Within this dynamic environment, candidates increasingly face challenges in orienting their career development, requiring them to continuously update their knowledge and capabilities to meet contemporary job requirements; this need is particularly necessary for new entrants to the labor market, who must cultivate a comprehensive understanding of current labor-market conditions. To address these issues, this study proposes an enterprise recruitment framework grounded in a career path knowledge graph, capturing occupations, skill requirements, and career transitions using standardized taxonomies enriched with job-posting data. The framework integrates transformer-based embeddings, large language models, and knowledge-graph reasoning to support efficient and reliable CV assessment, CV-JD matching and career guidance.

CareerPathKG: Knowledge Graph Integrated Framework for Career Intelligence

Personalized shopping agents must adapt their decisions to different user personas, balancing efficiency, preference alignment, and goal success. Building upon the WebShop dataset and τ^2-Bench environment, ShopperBench introduces a persona-guided benchmark for evaluating such adaptive behaviors. ShopperBench augments shopping trajectories with persona-conditioned goals, reasoning rationales, and preference cues, capturing how diverse shopper types---from price-conscious planners to trend-seeking explorers---navigate product search and selection. We further design a baseline of ShopperAgents that operate under persona guidance to simulate realistic, goal-oriented shopping interactions. To evaluate these agents, we propose new metrics including Persona Fidelity, Persona-Query Alignment, and Path Consistency. Together, Our ShopperBench provides a testbed for studying personalized and context-aware shopping intelligence, bridging the gap between human-centered e-commerce behavior and agent-based simulation.

ShopperBench: A Benchmark for Personalized Shopping with Persona-Guided Simulation

Finding optimal configurations for Retrieval-Augmented Generation (RAG) pipelines via grid search is computationally prohibitive, limiting real-world scalability. We investigate Bayesian Optimization (BO) as an efficient alternative, systematically comparing seven BO strategies combining four surrogate models and two multi-fidelity methods across FiQA, SciFact, and HotpotQA datasets. Our framework explores both global pipeline and local component-wise optimization, targeting final RAG performance and resource efficiency. Our results show that BO reduces optimization time by up to 84\% compared to grid search while maintaining comparable accuracy, with local optimization offering the most practical balance for deployment. Notably, performance gains plateau with larger evaluation budgets, suggesting that moderate resource investments suffice for effective RAG tuning. We provide actionable guidelines that empower industry practitioners to efficiently configure and deploy high-performing RAG systems under real-world constraints.

Beyond Grid Search: Leveraging Bayesian Optimization for Accelerating RAG Pipeline Optimization

The evaluation of Large Language Models (LLMs) remains challenging due to inconsistency, bias, and the absence of transparent decision criteria in automated judging. We present Debate, Deliberate, Decide (D3), a cost-aware, adversarial multi-agent framework that orchestrates structured debate among role-specialized agents (advocates, a judge, and an optional jury) to produce reliable and interpretable evaluations. D3 instantiates two complementary protocols: (1) Multi-Advocate One-Round Evaluation (MORE), which elicits k parallel defenses per answer to amplify signal via diverse advocacy, and (2) Single-Advocate Multi-Round Evaluation (SAMRE) with budgeted stopping, which iteratively refines arguments under an explicit token budget and convergence checks. We develop a probabilistic model of score gaps that (i) characterizes reliability and convergence under iterative debate and (ii) explains the separation gains from parallel advocacy. Under mild assumptions, the posterior distribution of the round- r gap concentrates around the true difference and the probability of mis-ranking vanishes; moreover, aggregating across k advocates provably increases expected score separation. We complement theory with a rigorous experimental suite across MT-Bench, AlignBench, and AUTO-J, showing state-of-the-art agreement with human judgments (accuracy and Cohen's), reduced positional and verbosity biases via anonymization and role diversification, and a favorable cost-accuracy frontier enabled by budgeted stopping. Ablations and qualitative analyses isolate the contributions of debate, aggregation, and anonymity. Together, these results establish D3 as a principled, practical recipe for reliable, interpretable, and cost-aware LLM evaluation.

Debate, Deliberate, Decide (D3): A Cost-Aware Adversarial Framework for Reliable and Interpretable LLM Evaluation

Ensuring that deep learning models are well-calibrated in terms of their predictive uncertainty is essential in maintaining their trustworthiness and reliability, yet despite increasing advances in foundation model research, the relationship between such large language models (LLMs) and their calibration remains an open area of research. In this work, we look at a critical gap in the calibration of LLMs within multilingual settings, in an attempt to better understand how the data scarcity can potentially lead to different calibration effects and how commonly used techniques can apply in these settings. Our analysis on two multilingual benchmarks, over 29 and 42 languages respectively, reveals that even in low-resource languages, model confidence can increase significantly after instruction-tuning on high-resource language SFT datasets. However, improvements in accuracy are marginal or non-existent, resulting in mis-calibration, highlighting a critical shortcoming of standard SFT for multilingual languages. Furthermore, we observe that the use of label smoothing to be a reasonable method alleviate this concern, again without any need for low-resource SFT data, maintaining better calibration across all languages. Overall, this highlights the importance of multilingual considerations for both training and tuning LLMs in order to improve their reliability and fairness in downstream use.

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