Morocco

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.

EACL 2026

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

conversational ner

large language models

knowledge distillation

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

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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. 

Workflow automation is critical for reducing manual efforts in industries, yet existing pipelines fail to handle generative tasks like summarization and extraction without pre-built tools, forcing human intervention. While LLM-based agents offer solutions, their creation depends heavily on prompt engineering—a resource-intensive process often yielding suboptimal results. Current automated approaches face a fundamental trade-off: discrete optimization produces overfitted prompts without convergence guarantees due to non-convex landscapes, while continuous gradient-based methods generate semantically incoherent prompts through embedding optimization. We propose CASPER, a framework bridging discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space. CASPER employs a feedback module producing detailed error analyses that capture failure modes as optimization signals. These insights are projected with prompt tokens into embedding space to steer gradient descent. To preserve interpretability, we incorporate fluency regularization that penalizes incomprehensible tokens. We further accelerate convergence through synthetic data generation that oversamples failure cases, while also addressing data scarcity in industrial settings. We evaluate CASPER on WDC, DROP, GSM8K with F1 improvements of 2.3\%, 1.6\%, 2.3\% and VQA, internal benchmarks showing accuracy improvements of 1.1\%, 3\%, demonstrating cross-domain generalizability.

CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent

Large Language Models (LLMs) offer strong generative capabilities, but many applications require explicit and \textit{fine-grained} control over specific textual concepts, such as humor, persuasiveness, or formality. Prior approaches in prompting and representation engineering can provide coarse or single-attribute control, but systematic evaluation of multi-attribute settings remains limited. We introduce an evaluation framework for fine-grained controllability for both single- and dual-concept scenarios, focusing on concept pairs that are linguistically distinct (e.g., persuasiveness vs.~humor). Surprisingly, across multiple LLMs and generative tasks, we find that performance often drops in the dual-concept setting, even though the chosen concepts should in principle be separable. This reveals a fundamental limitation of naive prompting-based control: models struggle with compositionality even when concepts are intuitively independent. Our framework provides systematic evidence of this gap and offers a principled approach for measuring the ability of future methods for multi-concept control.

Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs

Variation in human annotation (i.e., disagreements) is common in NLP, often reflecting important information like task subjectivity and sample ambiguity. Modeling this variation is important for applications that are sensitive to such information. Although RLVR-style reasoning (Reinforcement Learning with Verifiable Rewards) has improved Large Language Model (LLM) performance on many tasks, it remains unclear whether such reasoning enables LLMs to capture informative variation in human annotation. In this work, we evaluate the influence of different reasoning settings on LLM disagreement modeling. We systematically evaluate each reasoning setting across model sizes, distribution expression methods, and steering methods, resulting in 60 experimental setups across 3 tasks. Surprisingly, our results show that RLVR-style reasoning degrades performance in disagreement modeling, while naive Chain-of-Thought (CoT) reasoning improves the performance of RLHF LLMs (RL from human feedback). These findings underscore the potential risk of replacing human annotators with reasoning LLMs, especially when disagreements are important.

Can Reasoning Help Large Language Models Capture Human Annotator Disagreement?

End-to-end form filling refers to automatically populating fields in a document-style form with the appropriate information derived from external data. Although prevalent and useful, no formal benchmark exists for evaluating systems' form completion accuracy. Existing datasets focus on parsing, extraction and web form interaction, rather than end-to-end completion of document-style forms. We propose FormGym, a benchmark formulation of the end-to-end form filling task that evaluates form completion and accuracy. We construct FormGym by repurposing three existing datasets and add one new dataset to achieve more challenging, diverse, and realistic test cases. Our studies show baseline vision language agents (VLAs) perform poorly on FormGym in every scenario, primarily due to poor field localization. GUI agents perform better but suffer from high latency and costs. Therefore we also introduce FieldFinder, a field localization tool that enables zero-shot VLAs to find and accurately place text in input fields. We find that VLAs augmented with FieldFinder achieve better performance compared to baselines in all models.

FormGym: Doing Paperwork with Agents

Quantization is an effective technique for reducing the storage footprint and computational costs of Large Language Models (LLMs), but it often results in performance degradation. Existing post-training quantization methods typically use small, English-only calibration sets; however, their impact on multilingual models remains underexplored. We systematically evaluate eight calibration settings (five single-language and three multilingual mixes) across two quantizers (GPTQ, AWQ) on data from 10 different languages. Our findings reveal a consistent trend: non-English and multilingual calibration sets significantly improve perplexity compared to English-only baselines. Specifically, we observe notable average perplexity gains across both quantizers on Llama3.1 8B and Qwen2.5 7B, with multilingual mixes achieving the largest overall reductions of up to 3.52 perplexity gain. Furthermore, our analysis indicates that tailoring calibration sets to the evaluation language yields the largest improvements for individual languages, underscoring the importance of linguistic alignment. We also identify specific failure cases where certain language-quantizer combinations degrade performance, which we trace to differences in activation range distributions across languages. These results highlight that static, one-size-fits-all calibration is suboptimal, and that tailoring calibration data, both in language and diversity, plays a crucial role in robustly quantizing multilingual LLMs.

Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLMs

Large Language Models (LLMs) have achieved impressive performance yet remain inconsistent across languages, often defaulting to high-resource outputs such as English. Existing multilingual alignment methods mitigate these issues through preference optimization but rely on external supervision, such as translation systems or English-biased signal. We propose Multilingual Self-Alignment (MSA), a targeted preference optimization framework that leverages an LLM's own latent representations as intrinsic supervision signals, rewarding lower-resource language outputs based on their alignment with high-resource (English) counterparts in the "semantic hub". We further introduce Language-Consistency MSA (LaCoMSA), which augments MSA with a final-layer language-consistency factor to prevent off-target generation. Integrated with Direct Preference Optimization, LaCoMSA improves a Llama 3 8B-based model multilingual win rates by up to 6.8% absolute (55.0% relatively) on X-AlpacaEval and achieves consistent gains across benchmarks and models. Our findings demonstrate that LaCoMSA can serve as an effective and scalable mechanism, opening a new venue toward multilingual self-alignment.

LaCoMSA: Language-Consistency Multilingual Self-Alignment with Latent Representation Rewarding

Retrieval Augmented Generation (RAG) is a popular method for injecting up-to-date information into Large Language Model (LLM)-based Natural Language Generation (NLG) systems. While RAG can enhance factual accuracy, its effect on the social biases inherent in LLMs is not well understood. This paper systematically investigates how RAG modulates social biases across three languages (English, Japanese, and Chinese) and four categories (gender, race, age, and religion). By evaluating various generator LLMs on the BBQ benchmark, we analyse how document collections with controlled stereotypical content affect RAG outputs. We find that biases present in the retrieved documents are often significantly amplified in the generated texts, even when the base LLM itself has a low-level of intrinsic bias. These findings raise concerns about the social fairness of RAG systems, underscoring the urgent need for careful bias evaluation before real-world deployment.

Evaluating the Effect of Retrieval Augmentation on Social Biases

The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but may fail in the real world. We introduce ConvApparel, a new dataset of human-AI conversations designed to better understand this gap. Its unique dual-agent data collection protocol, using both "good" and "bad" recommenders, enables counterfactual validation by capturing a wide spectrum of user experiences, enriched with first-person annotations of user satisfaction. We propose a comprehensive validation framework that combines statistical alignment, a human-likeness score, and counterfactual validation to test for generalization. Our experiments reveal a significant realism gap across all simulators. However, the framework also shows that data-driven methods consistently outperform a prompted baseline, particularly in counterfactual validation where they adapt more realistically to unseen behaviors, indicating a more robust, if imperfect, user model.

ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders

Large language models (LLMs) have been reported to implicitly encode character-level information, despite not being explicitly provided during training. However, the mechanisms underlying this phenomenon remain largely unexplored. To reveal the mechanisms, we analyze how models acquire character-level knowledge by comparing LLMs trained under controlled settings, such as specifying the pre-training dataset or tokenizer, with those trained under standard settings. We categorize the contributing factors into those independent of tokenization. Our analysis reveals that merge rules and orthographic constraints constitute primary factors arising from tokenization, whereas semantic associations of substrings and syntactic information function as key factors independent of tokenization.

How Do Language Models Acquire Character-Level Information?

Logical fallacies are common in public communication and can mislead audiences; fallacious arguments may still appear convincing despite lacking soundness, because convincingness is inherently subjective. We present the first computational study of how emotional framing interacts with fallacies and convincingness, using large language models (LLMs) to systematically change emotional appeals in fallacious arguments. We benchmark eight LLMs on injecting emotional appeal into fallacious arguments while preserving their logical structures, then use the best models to generate stimuli for a human study. Our results show that LLM-driven emotional framing reduces human fallacy detection in F1 by 14.5% on average. Humans perform better in fallacy detection when perceiving enjoyment than fear or sadness, and these three emotions also correlate with significantly higher convincingness compared to neutral or other emotion states. Our work has implications for AI-driven emotional manipulation in the context of fallacious argumentation.

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CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent

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Simple Questions Generate Named Entity Recognition Datasets

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Next from EACL 2026

CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent

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Simple Questions Generate Named Entity Recognition Datasets

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PRESENTATIONS

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