China

We present CorPipe 25, the winning entry to the CRAC 2025 Shared Task on Multilingual Coreference Resolution. This fourth iteration of the shared task introduces a new LLM track alongside the original unconstrained track, features reduced development and test sets to lower computational requirements, and includes additional datasets. CorPipe 25 represents a complete reimplementation of our previous systems, migrating from TensorFlow to PyTorch. Our system significantly outperforms all other submissions in both the LLM and unconstrained tracks by a substantial margin of 8 percentage points. The source code and trained models are publicly available at https://github.com/ufal/crac2025-corpipe.

EMNLP 2025

CorPipe at CRAC 2025: Evaluating Multilingual Encoders for Multilingual Coreference Resolution

multilingual coreference resolution. corefud

crac

corpipe

shared task

coreference resolution

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

workshop paper

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Gundel et al.'s Givenness Hierarchy remains one of the most influental frameworks of Information Status to this date, and has been employed in different technical contexts to account for context-sensitive and hearer-tailored language in human-machine interaction and natural language processing as well as as a topic of linguistic inquiry. At the same time, the data basis upon which this theory has been developed remains relatively thin. Although its applicability to a broad array of languages has been repeatedly confirmed, the empirical evidence presented for certain phenomena, and in particular, with respect to demonstrative determiners and demonstrative pronouns did not always reach conventional levels of statistical significance. In this paper, we provide an empirical, corpus-based re-assessment of two seminal papers for the Givenness Hierarchy, Gundel et al. (1990) and Gundel et al. (1993), where we aim to replicate their findings on the basis of corpora with coreference annotation for their original sample of languages, i.e., Arabic, Chinese, English, Japanese, Korean, Russian and Spanish. We describe the operationalization of Gundel et al.'s `cognitive statuses', their approximation by means of anaphoric relations, the preprocessing of diverse and heterogeneous corpora and evaluate Gundel et al.'s claims. Our contribution is three-fold: We evaluate the Givenness Hierarchy against quantitative data at a scale that allows to assess statistical significance, we discuss challenges and problems encountered in the process, in the preprocessing and in the interpretation of the diverse corpora, we provide two generalizations: a procedure for bootstrapping Givenness Hierarchies for other languages, and possible cross-linguistically applicable tendencies in the systems of referring expressions.

Revisiting the Givenness Hierarchy. A Corpus-Based Evaluation

We tackle the task of mention detection for pair-programming dialogue, a setting which adds several challenges to the task due to the characteristics of natural dialogue, the dynamic environment of the dialogue task, and the domain-specific vocabulary and structures. We compare recent variants of the Llama and GPT families and explore different prompt and context engineering approaches. While aspects like hesitations and references to read-out code and variable names made the task challenging, GPT 4.1 approximated human performance when we provided few-shot examples similar to the inference text and corrected formatting errors.

Mention detection with LLMs in pair-programming dialogue

While coreference resolution is attracting more interest than ever from computational literature researchers, representative datasets of fully annotated long documents remain surprisingly scarce. In this paper, we introduce a new annotated corpus of three full-length French novels, totaling over 285,000 tokens. Unlike previous datasets focused on shorter texts, our corpus addresses the challenges posed by long, complex literary works, enabling evaluation of coreference models in the context of long reference chains. We present a modular coreference resolution pipeline that allows for fine-grained error analysis. We show that our approach is competitive and scales effectively to long documents. Finally, we demonstrate its usefulness to infer the gender of fictional characters, showcasing its relevance for both literary analysis and downstream NLP tasks.

The Elephant in the Coreference Room: Resolving Coreference in Full-Length French Fiction Works

Training models that can perform well on various NLP tasks requires large amounts of data, which becomes even more apparent with more nuanced tasks such as anaphora and coreference resolution. This paper presents the automatic creation of an Arabic CorefUD dataset through the automatic conversion of the existing gold-annotated OntoNotes.

Towards Adding Arabic to CorefUD

In recent years, research on sign languages has attracted increasing attention in the NLP community and requires more effort from a linguistic perspective. In this paper, we explore coreference resolution in German Sign Language (GSL) primarily through gloss-based analysis. Specifically, in GSL glosses, we conduct a linguistic analysis of coreference, add coreference annotations based on one video, and evaluate the ability of two large language models to resolve coreference. We gain valuable insights into coreference resolution in GSL, which pave the way for future research.

Exploring Coreference Resolution in Glosses of German Sign Language

This study introduces a new ASR-transcribed coreference corpus for French and explores the transferability of coreference resolution models from human-transcribed to ASR-transcribed data. Given the challenges posed by differences in text characteristics and errors introduced by ASR systems, we evaluate model performance using newly constructed parallel human-ASR silver training and gold validation datasets. Our findings show a decline in performance on ASR data for models trained on manual transcriptions. However, combining silver ASR data with gold manual data enhances model robustness. Through detailed error analysis, we observe that models emphasizing recall are more resilient to ASR-induced errors compared to those focusing on precision. The resulting ASR corpus, along with all related materials, is freely available under the CC BY-NC-SA 4.0 license at: https://github.com/ina-foss/french-asr-coreference.

Impact of ASR Transcriptions on French Spoken Coreference Resolution

The paper presents an overview of the fourth edition of the Shared Task on Multilingual Coreference Resolution, organized as part of the CODI-CRAC 2025 workshop. As in the previous editions, participants were challenged to develop systems that identify mentions and cluster them according to identity coreference. A key innovation of this year's task was the introduction of a dedicated Large Language Model (LLM) track, featuring a simplified plaintext format designed to be more suitable for LLMs than the original CoNLL-U representation. The task also expanded its coverage with three new datasets in two additional languages, using version 1.3 of CorefUD -- a harmonized multilingual collection of 22 datasets in 17 languages. In total, nine systems participated, including four LLM-based approaches (two fine-tuned and two using few-shot adaptation). While traditional systems still kept the lead, LLMs showed clear potential, suggesting they may soon challenge established approaches in future editions.

Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?

Culturally grounded commonsense reasoning is underexplored in low-resource languages due to scarce data and costly native annotation. We test whether large language models (LLMs) can generate culturally nuanced narratives for such settings. Focusing on Javanese and Sundanese, we compare three data creation strategies: (1) LLM-assisted stories prompted with cultural cues, (2) machine translation from Indonesian benchmarks, and (3) native-written stories. Human evaluation finds LLM stories match natives on cultural fidelity but lag in coherence and correctness. We fine-tune models on each dataset and evaluate on a human-authored test set for classification and generation. LLM-generated data yields higher downstream performance than machine-translated and Indonesian human-authored training data. We release a high-quality benchmark of culturally grounded commonsense stories in Javanese and Sundanese to support future work.

Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese

We present an uncertainty‐based approach to Partial Diacritization (PD) for Arabic text. We evaluate three uncertainty metrics for this task: Softmax Response, BALD via MC-dropout, and Mahalanobis Distance. We further introduce a lightweight Confident Error Regularizer to improve model calibration. Our preliminary exploration illustrates possible ways to use uncertainty estimation for selectively retaining or discarding diacritics in Arabic text with an analysis of performance in terms of correlation with diacritic error rates. For instance, the model can be used to detect words with high diacritic error rates which tend to have higher uncertainty scores at inference time. On the Tashkeela dataset, the method maintains low Diacritic Error Rate while reducing the amount of visible diacritics on the text by up to 50% with thresholding-based retention.

Uncertainty-driven Partial Diacritization for Arabic Text

Autoregressive language models achieve strong performance across a wide range of natural language processing (NLP) tasks, yet their uncertainty estimates remain poorly understood, particularly during training. Prior work has primarily evaluated calibration and out-of-distribution (OOD) robustness at the final checkpoint, overlooking the dynamics that unfold earlier. We introduce a phase-based framework for tracking uncertainty metrics—including expected calibration error (ECE) and Kullback–Leibler (KL) divergence—across distinct stages of training. Using GPT-2 models trained across multiple random seeds, we find that uncertainty dynamics follow a consistent set of phases: models begin conservative and relatively well calibrated, but later phases introduce a paradoxical decoupling where confidence increases even as calibration worsens, especially under distribution shift. This paradox implies that the final checkpoint is not always the most reliable for deployment and motivates phase-aware strategies such as dynamic checkpoint selection or targeted calibration. Our findings highlight that uncertainty should be understood as a training-dependent property rather than a static one, opening new directions for scaling this framework to larger models, tasks, and distribution shift scenarios.

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