Morocco

Quality estimation is omnipresent in machine translation, for both evaluation and generation. Unfortunately, quality estimation models are often opaque and computationally expensive, making them impractical to be part of large-scale pipelines. In this work, we tackle two connected challenges: (1) reducing the cost of quality estimation at scale, and (2) developing an inexpensive uncertainty estimation method for quality estimation. To address the latter, we introduce Instant Confidence COMET, an uncertainty-aware quality estimation model that matches the performance of previous approaches at a fraction of their costs. We extend this to Early-Exit COMET, a quality estimation model that can compute quality scores and associated confidences already at early model layers, allowing us to early-exit computations and reduce evaluation costs. We also apply our model to machine translation reranking. We combine Early-Exit COMET with an upper confidence bound bandit algorithm to find the best candidate from a large pool without having to run the full evaluation model on all candidates. In both cases (evaluation and reranking) our methods reduce the required compute by 50% with very little degradation in performance. Finally, we show how Instant Confidence COMET can be used to decide which translations a human evaluator should score rather than relying on the COMET score.

EACL 2026

Early-Exit and Instant Confidence Translation Quality Estimation

automatic metrics

efficiency

translation

quality estimation

evaluation

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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. 

Multi-modal summarization (MMS) has emerged as a critical research area driven by the proliferation of multimedia content, focusing on generating condensed summaries by cross-modal complementary information synthesis. Previous studies have demonstrated the effectiveness of heterogeneous fusion paradigms, particularly through visual-centric feature extraction mechanisms, in constructing cross-modal representations that yield substantial performance gains. However, the use of multi-modal information and the inter-correlation among textual content, visual elements, and summary generation are still underestimated. We propose the Patch-Refined Visual Information Network (PRVIN) to address the insufficient exploitation of visual information. The essential patch selector and patch refiner components in PRVIN work collaboratively to progressively identify and refine critical visual features. An additional vision-to-summary alignment mechanism is also introduced to enhance the semantic connections between multi-modal representations and summary outputs. Extensive experiments conducted on two public MMS benchmark datasets demonstrate the superiority of PRVIN while quantitatively validating the crucial role of comprehensive visual information utilization in MMS tasks.\footnote{Our code will be available at

Progressive Visual Refinement for Multi-modal Summarization

Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature, i.e., the integration of a target solver is predetermined at design time, hindering the ability to employ diverse formal inference strategies. To address this, we introduce an adaptive, multi-paradigm, neuro-symbolic inference framework that: (1) automatically identifies formal reasoning strategies from problems expressed in natural language; and (2) dynamically selects and applies specialized formal logical solvers via autoformalization interfaces. Extensive experiments on individual and multi-paradigm reasoning tasks support the following conclusions: LLMs are effective at predicting the necessary formal reasoning strategies with an accuracy above 90%. This enables flexible integration with formal logical solvers, resulting in our framework outperforming competing baselines -- by 17% and 6% compared to GPT-4o and DeepSeek-V3.1, respectively. Moreover, adaptive reasoning can even positively impact pure LLM methods, yielding gains of 10%, 5%, and 6% on zero-shot, CoT, and CoT sym settings with GPT-4o. Finally, although smaller models struggle with adaptive neuro-symbolic reasoning, post-training offers a viable path to improvement. Overall, this work establishes the foundations for adaptive LLM-symbolic reasoning, offering a path forward for unifying material and formal inferences on heterogeneous reasoning challenges. The code and data are available [online](https://github.com/idiap/adaptive symbolic reasoning).

Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition

Large reasoning models are trained to solve problems by decomposing them into steps. While they show impressive progress on reasoning tasks, "reasoning" here is typically limited to formal reasoning, i.e., math, code, and logic. An open question is whether these abilities transfer to pronominal reasoning, where step-by-step thinking in non-reasoning models worsens performance, but code pre-training may help. I answer this question by evaluating six pairs of original and DeepSeek-distilled models (1.5B-70B parameters) on six challenging datasets for English pronoun resolution (identifying whom a pronoun refers to) and pronoun fidelity (learning and applying a pronoun mapping correctly). Performance improves statistically significantly on all datasets (31% relative increase), indicating that distilling step-by-step formal reasoning does in fact help with pronominal reasoning, in part by improving instruction-following. With a qualitative evaluation of 720 generations, I show that improvements occur across granular error types, and come from plausible-looking reasoning chains employing a variety of reasoning strategies. However, the gains put models just above random performance on these datasets, leaving plenty of room for improvement.

Training in Step-by-Step Formal Reasoning Improves Pronominal Reasoning in Language Models

Strongly human-correlated evaluation metrics serve as an essential compass for the development and improvement of generation models, and they must be highly reliable and robust. Recent embedding-based neural text evaluation metrics, such as COMET for the translation task, are widely used in both research and development fields. However, there is no guarantee that they yield reliable evaluation results due to the black-box nature of neural networks. To raise concerns about the reliability and safety of such metrics, we propose a method for creating a single adversarial text in the discrete space that is consistently evaluated as high-quality, regardless of the test cases, in order to identify the vulnerabilities in evaluation metrics. Our found single hub text achieved 83.1 and 79.1 COMET% on the WMT'23 and WMT'24 English-to-Japanese translation tasks, respectively, outperforming translations generated individually for each source sentence by M2M100, a general translation model. Furthermore, we confirmed that our created single hub text generalizes across multiple language pairs such as Ja--En, En--De, and De--En.

Hacking Neural Evaluation Metrics with a Single Text

Novelty assessment is a central yet understudied aspect of peer review, particularly in high-volume fields like NLP where reviewer capacity is increasingly strained. We present a structured approach for automated novelty evaluation that models expert reviewer behavior through three stages: (i) content extraction from submissions, (ii) retrieval and synthesis of related work, and (iii) structured comparison for evidence-based assessment. Our method is informed by analysis of human-written novelty reviews and captures key patterns such as independent claim verification and contextual reasoning. Evaluated on 182 ICLR 2025 submissions with human-annotated reviewer novelty assessments, the approach achieves 86.5% alignment with human reasoning and 75.3% agreement on novelty conclusions, substantially outperforming existing LLM-based baselines. It produces detailed, literature-aware analysis and improves consistency over ad hoc reviewer judgments. These results highlight the potential for structured LLM-assisted approaches to support more rigorous and transparent peer review without displacing human expertise. The data and the code are available at https://ukplab.github.io/eacl2026-assessing-paper-novelty/

Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback

Large Vision-Language Models (LVLMs), trained by aligning visual encoders to LLMs on extensive vision-language data, demonstrate impressive performance across a broad variety of tasks that require understanding of both visual and textual inputs. Acknowledging this, recent work proposed to post-hoc convert generative LVLMs into vision-language encoders (VLEs) via supervised contrastive learning objectives. This type of training enables LVLMs to produce better representations, i.e., embeddings for image and text input, used in retrieval and (semantic) similarity tasks. Having observed that this type of VLEs (i.e., LVLMs turned into encoders) commonly employ last-token pooling in downstream tasks, without using special sequence-end tokens, in this focused contribution, we study the effect of pooling strategies on VLEs' downstream performance. We empirically show that, in contrast to mean pooling, last-token pooling (without special sequence-end tokens) makes VLEs highly sensitive to end-of-input artifacts in fine-tuning and inference data, e.g., whether input sequences end with punctuation or newline characters. Finally, we show that introducing the special end-of-sequence token removes this sensitivity and makes VLEs robust to formatting artifacts of input text.

Mind Your Special Tokens! On the Importance of Dedicated Sequence-End Tokens in Vision-Language Embedding Models

Large language models (LLMs) can be benchmark-contaminated, which produces inflated scores that mask memorization as generalization. In a multilingual setting, memorization has been shown to be able to transfer to ``uncontaminated'' languages. Using the FLORES-200 machine translation benchmark as a diagnostic, we study three 7--8B instruction-tuned multilingual LLMs. Using Llama and Qwen's BLEU and COMET scores as a control, we confirm Bloomz's FLORES contamination. We then demonstrate that machine translation contamination happens cross-lingually and is driven by target-side memorization, artificially boosting performance in translating unseen input. Further analysis shows that despite our source paraphrasing or perturbation efforts, recall of memorized references often persists. We discover that this is strongly anchored to source-side named entities; randomizing these sharply reduces recall of memorized texts. This provides insights into potential ways to cleanly benchmark contaminated LLMs.

When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation

Meta-reviewing is a pivotal stage in the peer-review process, serving as the final step in determining whether a paper is recommended for acceptance. Prior research on meta-reviewing has treated this as a summarization problem over review reports. However, complementary to this perspective, meta-reviewing is a decision-making process that requires weighing reviewer arguments and placing them within a broader context. Prior research has demonstrated that decision-makers can be effectively assisted in such scenarios via dialogue agents. In line with this framing, we explore the practical challenges for realizing dialog agents that can effectively assist meta-reviewers. Concretely, we first address the issue of data scarcity for training dialogue agents by generating synthetic data using Large Language Models (LLMs) based on a self-refinement strategy to improve the relevance of these dialogues to expert domains. Our experiments demonstrate that this method produces higher-quality synthetic data and can serve as a valuable resource towards training meta-reviewing assistants. Subsequently, we utilize this data to train dialogue agents tailored for meta-reviewing and find that these agents outperform *off-the-shelf* LLM-based assistants for this task. Finally, we apply our agents in real-world meta-reviewing scenarios and confirm their effectiveness in enhancing the efficiency of meta-reviewing.

Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue

Text-to-image (T2I) models are increasingly employed by users worldwide. However, prior research has pointed to the high sensitivity of T2I towards particular input languages - when faced with languages other than English (i.e., different surface forms of the same prompt), T2I models often produce culturally stereotypical depictions, prioritizing the surface over the prompt's semantics. Yet a comprehensive analysis of this behavior, which we dub Surface-over-Semantics (SoS), is missing. We present the first analysis of T2I models' SoS tendencies. To this end, we create a set of prompts covering 171 cultural identities, translated into 14 languages, and use it to prompt seven T2I models. To quantify SoS tendencies across models, languages, and cultures, we introduce a novel evaluation measure and analyze how the tendencies we identify manifest visually. We show that all tested models exhibit strong surface tendencies in at least three languages, and that this effect intensifies throughout the layers of T2Is' text encoders. Furthermore, strong surface tendencies often directly relate to stereotypical depictions and are reflected in distinct color profiles.

SoS: Analysis of Surface over Semantics in Multilingual Text-To-Image Generation

Process reward models (PRMs) improve complex reasoning in large language models (LLMs) by grading candidate solutions step-by-step and selecting answers via aggregated step scores. While effective in domains such as mathematics, their applicability to tasks involving semi-structured data, like table question answering (TQA) remains unexplored. TQA poses unique challenges for PRMs, including abundant irrelevant information, loosely connected reasoning steps, and domain-specific reasoning. This work presents the first systematic study of PRMs for TQA. We evaluate state-of-the-art generative PRMs on TQA from both answer and step perspectives. Results show that PRMs that combine textual and code verification can aid solution selection but struggle to generalize to out-of-domain data. Analysis reveals a weak correlation between performance in step-level verification and answer accuracy, possibly stemming from weak step dependencies and loose causal links. Our findings highlight limitations of current PRMs on TQA and offer valuable insights for building more robust, process-aware verifiers.

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Progressive Visual Refinement for Multi-modal Summarization

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