Morocco

Evaluating Retrieval-Augmented Generation
(RAG) systems remains a challenging task: existing
metrics often collapse heterogeneous behaviors
into single scores and provide little insight
into whether errors arise from retrieval,
reasoning, or grounding. In this paper, we introduce
RAGVUE, a diagnostic and explainable
framework for automated, reference-free
evaluation of RAG pipelines. RAGVUE decomposes
RAG behavior into retrieval quality,
answer relevance and completeness, strict
claim-level faithfulness, and judge calibration.
Each metric includes a structured explanation,
making the evaluation process transparent. Our
framework supports both manual metric selection
and fully automated agentic evaluation. It
also provides a Python API, CLI, and a local
Streamlit interface for interactive usage. In
comparative experiments, RAGVUE surfaces
fine-grained failures that existing tools such
as RAGAS often overlook. We showcase the
full RAGVUE workflow and illustrate how it
can be integrated into research pipelines and
practical RAG development. The source code
and detailed instructions on usage are publicly
available on Github.

EACL 2026

RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation

evidence attribution.

rag pipeline analysis

diagnostic metrics

automated rag evaluation

retrieval-augmented generation

explainable evaluation

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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EACL 2024

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

demo

#### *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. 

Negotiation is a fundamental challenge for AI agents, as it requires an ability to reason strategically, model opponents, and balance cooperation with competition. We conduct the first comprehensive study systematically evaluating the effect of (LLM-)reasoning on the negotiation abilities of both commercial and open-weight LLMs, and do this across three languages. Using a self-play setup across three diverse dialogue games, we analyse trade-offs between performance and cost, the language consistency of reasoning processes, and the nature of strategic adaptation exhibited by models. Our findings show that enabling reasoning - that is, scaling test time compute -significantly improves negotiation outcomes by enhancing collaboration and helping models overcome task complexities, but comes at a substantial computational cost: reasoning improves GPT-5's performance by 31.4 % while increasing its cost by nearly 400 %. Most critically, we uncover a significant multilingual reasoning distinction: open-weight models consistently switch to English for their internal reasoning steps, even when negotiating in German or Italian (and thus possibly impacting potential explainability gains through the disclosure of reasoning traces), while leading commercial models maintain language consistency between their reasoning and final output.

The Price of Thought: A Multilingual Analysis of Reasoning, Performance, and Cost of Negotiation in Large Language Models

As a digraphic language, the Persian language utilizes two written standards: Perso-Arabic in Afghanistan and Iran, and Tajik-Cyrillic in Tajikistan. Despite the significant similarity between the dialects of each country, script differences prevent simple one-to-one mapping, hindering written communication and interaction between Tajikistan and its Persian-speaking ``siblings''. To overcome this, previously-published efforts have investigated machine transliteration models to convert between the two scripts. Unfortunately, most efforts did not use datasets other than those they created, limiting these models to certain domains of text such as archaic poetry or word lists. A truly usable transliteration system must be capable of handling varied domains, meaning that suck models lack the versatility required for real-world usage. The contrast in domain between data also obscures the task's true difficulty. We present a new state-of-the-art sequence-to-sequence model for Tajik-Farsi transliteration trained across all available datasets, and present two datasets of our own. Our results across domains provide clearer understanding of the task, and set comprehensive comparable leading benchmarks. Overall, our model achieves chrF++ and Normalized CER scores of 87.91 and 0.05 from Farsi to Tajik and 92.28 and 0.04 from Tajik to Farsi. Our model, data, and code are available at https://anonymous.4open.science/r/ParsTranslit-FB30/.

ParsTranslit: Truly Versatile Tajik-Farsi Transliteration

Detecting persuasion in argumentative text is a challenging task with important implications for understanding human communication. This work investigates the role of persuasion strategies - such as Attack on reputation, Distraction, and Manipulative wording - in determining the persuasiveness of a text. We conduct experiments on three annotated argument datasets: Winning Arguments (built from the Change My View subreddit), Anthropic/Persuasion, and Persuasion for Good. Our approach leverages large language models (LLMs) with a chain-of-thought framework that guides reasoning over six persuasion strategies. Results show that strategy-guided reasoning improves the prediction of persuasiveness. To better understand the influence of content, we organize the Winning Argument dataset into broad discussion topics and analyze performance across them. We publicly release this topic-annotated version of the dataset to facilitate future research. Overall, our methodology demonstrates the value of structured, strategy-aware prompting for enhancing interpretability and robustness in argument quality assessment.

Detecting Winning Arguments with Large Language Models and Persuasion Strategies

Role-play prompting is known to steer the behavior of language models by injecting a persona into the prompt, improving their zero-shot reasoning capabilities. However, such improvements are inconsistent across different tasks or instances. This inconsistency suggests that zero-shot and role-play prompting may offer complementary strengths rather than one being universally superior. Building on this insight, we propose **Persona Switch**, a novel decoding method that dynamically combines the benefits of both prompting strategies. Our method proceeds step-by-step, selecting the better output between zero-shot and role-play prompting at each step by comparing their output confidence, as measured by the logit gap. Experiments with widely-used LLMs demonstrate that Persona Switch consistently outperforms competitive baselines, achieving up to 5.13\% accuracy improvement. Furthermore, we show that output confidence serves as an informative measure for selecting the more reliable output.

Persona Switch: Mixing Distinct Perspectives in Decoding Time

Understanding user intent is essential for effective planning in conversational assistants, particularly those powered by large language models (LLMs) coordinating multiple agents. However, real-world dialogues are often ambiguous, underspecified, or dynamic, making intent understanding a persistent challenge. Traditional classification-based approaches struggle to generalize in open-ended settings, leading to brittle interpretations and poor downstream planning. We propose RECAP (REwriting Conversations for Agent Planning), a new benchmark designed to evaluate and advance intent rewriting, reframing user-agent dialogues into concise representations of user goals. RECAP captures diverse challenges such as ambiguity, intent drift, vagueness, and mixed-goal conversations. Alongside the dataset, we introduce an LLM-based evaluator that compares planning utility given a user-agent dialogue. Using RECAP, we develop a prompt-based rewriting approach that outperforms baselines, in terms of plan preference. We further demonstrate that fine-tuning two DPO-based rewriters yields additional utility gains. Our results highlight intent rewriting as a critical and tractable component for improving agentic planning in open-domain dialogue systems.

RECAP: REwriting Conversations for Intent Understanding in Agentic Planning

Evaluating the quality of open-domain chatbots has become increasingly reliant on LLMs acting as automatic judges. However, existing meta-evaluation benchmarks are static, outdated, and lacking in multilingual coverage, limiting their ability to fully capture subtle weaknesses in evaluation. We introduce MEDAL, an automated multi-agent framework for curating more representative and diverse open-domain dialogue evaluation benchmarks. Our approach leverages several LLMs to generate user-chatbot multilingual dialogues, conditioned on varied seed contexts. Then, a state-of-the-art LLM (GPT-4.1) is used for a multidimensional analysis of the performance of the chatbots, uncovering noticeable cross-lingual performance differences. Guided by this large-scale evaluation, we curate a new meta-evaluation multilingual benchmark and human-annotate samples with nuanced quality judgments. This benchmark is then used to assess the ability of several reasoning and non-reasoning LLMs to act as evaluators of open-domain dialogues. Using MEDAL, we uncover that state-of-the-art judges fail to reliably detect nuanced issues such as lack of empathy, common sense, or relevance.

MEDAL: A Framework for Benchmarking LLMs as Multilingual Open-Domain Dialogue Evaluators

Massively multilingual language models enable cross-lingual generalization but underperform on low-resource and unseen languages. While adapter-based fine-tuning offers a parameter-efficient solution, training language-specific adapters at scale remains costly. We introduce Typologically Informed Parameter Aggregation (TIPA), a training-free framework that constructs proxy language adapters by aggregating existing ones, weighted by typological similarity. Integrated into the MAD-X architecture, these proxies enable zero-shot cross-lingual transfer without additional training. We evaluate TIPA on five NLP tasks and over 230 languages. TIPA consistently outperforms baselines such as English-only fine-tuning and selecting the typologically closest-language adapter, with the largest gains for languages lacking dedicated adapters. Our results demonstrate that typologically informed aggregation provides a viable alternative to language-specific modules without any training needed.

TIPA: Typologically Informed Parameter Aggregation

Recent multilingual named entity recognition (NER) work has shown that large language models (LLMs) can provide effective synthetic supervision, yet such datasets have mostly appeared as by-products of broader experiments rather than as systematic, reusable resources. We introduce FiNERweb, a dataset-creation pipeline that scales the teacherвЂ“student paradigm to 91 languages and 25 scripts. Building on FineWeb-Edu, our approach trains regression models to identify NER-relevant passages and annotates them with multilingual LLMs, resulting in about 225k passages with 235k distinct entity labels. We train a student model on this dataset to enable the research community to efficiently expand annotations in their desired languages. The regression model for filtering useful NER passages achieves more than 84 F1. We assess annotation quality using LLM-based ratings, which show high faithfulness (3.99/5) and completeness (4.05/5). We further translate the label set into the respective target languages and observe a performance decrease of 0.02-0.09 F1. This result shows that many multilingual models rely heavily on English label prompts and that improved cross-lingual alignment is essential for future work.

FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition

Annotated data scarcity has long hindered progress in dialogue discourse parsing. To fill this gap, we introduce MIMIC, a framework for augmenting discourse-annotated corpora via speaker stylistic transfer using Large Language Models (LLMs). MIMIC rephrases utterances while preserving discourse coherence, using the MASK metric to identify speakers for replacement that enrich structural diversity and the MIRROR method to select substitute speakers who have experienced similar discourse interactions. Experimental results on STAC and Molweni corpora show that parsers trained with MIMIC-augmented data improve both link prediction and relation classification, with consistent gains for underrepresented discourse patterns and in low-resource scenarios.

MIMIC: Multi-party Dialogue Augmentation via Speaker Stylistic Transfer

Deep search, which aims to answer complicated questions requiring searching for and reasoning across multiple documents, is becoming one of the most important agentic use-cases. Yet, it is not scalable to leverage human annotations to collect deep search data due to long and complex exploration trajectories. We propose an agentic pipeline to automatically generate high-quality deep search question answer pairs for a given corpus. Our initial data generator agent is able to produce QA pairs that need multiple rounds of search based on an input document. To further improve the steer-ability of our data generation framework, we 1) add a natural language prompt to specify a level of difficulty; 2) incorporate the execution traces from a task execution agent as feedback to correct undesirable data. Our intrinsic evaluation shows our framework significantly increases the correctness and difficulty of the generated data. Our extrinsic evaluation demonstrates that compared to training with existing large-scale training data, our data improved the trained search agent with >15% gains across in-domain and out-of-domain complex question answering datasets on two model sizes. Analysis shows that our pipeline generates questions that require diverse reasoning strategies.

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