United States

Participating again in this year&#39;s edition of the CRAC shared task on coreference resolution, we present our upgraded system with an official uplift of 15.46 percentage points in CoNLL-U score. We incorporate the larger Gemma 3 27B IT model, joint pre-training, headword tagging, more efficient training and inference as well as a sliding window to achieve this result. Our system placed second in the LLM track and third overall with a primary score of 73.83. We achieved the highest scores on two datasets. Finally, we compare specialized and general LLM approaches.

ACL 2026

Generative Multilingual Coreference Resolution at CRAC 2026

qlora

corefud

text-to-text

zero anaphora

large language models

coreference resolution

fine-tuning

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

Participating again in this year's edition of the CRAC shared task on coreference resolution, we present our upgraded system with an official uplift of 15.46 percentage points in CoNLL-U score. We incorporate the larger Gemma 3 27B IT model, joint pre-training, headword tagging, more efficient training and inference as well as a sliding window to achieve this result. Our system placed second in the LLM track and third overall with a primary score of 73.83. We achieved the highest scores on two datasets. Finally, we compare specialized and general LLM approaches.

workshop paper

We present DAggerCoref, our submission to
the CRAC 2026 Shared Task on Multilingual
Coreference Resolution. DAggerCoref is a
three-stage cascade built on XLM-RoBERTa-
large: a gap classifier for zero pronoun detec-
tion, a mention head classifier, and a coarse-
to-fine antecedent scorer. Our central contri-
bution is applying DAgger (Ross et al., 2011)
to coreference resolution: after training the
antecedent scorer on gold mentions, we fine-
tune on a 50/50 mix of gold and pipeline-
predicted mentions, closing the train/test dis-
tribution mismatch and improving develop-
ment set macro CoNLL F1 by 1.10 points. We
also introduce Otsu adaptive thresholding for
zero pronoun detection, which matches gold-
tuned per-dataset thresholds without requiring
any gold supervision. Our system achieves a
macro CoNLL F1 of 67.56 on the official test
set across 27 datasets and 19 languages

Closing the Gap: Robust Multilingual Coreference Resolution with DAgger

We describe PortNLP's submission to the CRAC 2026 Shared Task on Multilingual Coreference Resolution (LLM track). Our system fine-tunes Qwen 3 14B with QLoRA on CorefUD 1.4 gold annotations across 27 corpora spanning 19 languages. Documents are processed in 500-700 character chunks with a bounded rolling context consisting of 500 characters of recent annotated text and a scored entity registry that tracks up to 30 active entities via a frequency-times-recency decay formula. We employ data augmentation and language-aware sampling strategies to handle typological and data-size diversity. Our system achieves 68.69 CoNLL F1 averaged across all 27 test corpora. We additionally present probing experiments on the LoRA adapter's internal representations, finding that coreference signal is concentrated in attention value projections rather than MLP modules, with the strongest readout at the earliest transformer layer.

PortNLP at CRAC 2026: QLoRA Fine-Tuning with Bounded Entity Registry for Multilingual Coreference Resolution

We introduce CorPipe 26, our winning submission to the CRAC 2026 Shared Task on Multilingual Coreference Resolution. The fifth edition of this shared task focuses mainly on the comparison of generative LLMs and specialized systems; additionally, 5 more datasets and 2 new languages are introduced. CorPipe 26 is an improved version of CorPipe 25, with a new variant predicting empty nodes together with mentions and coreference links in a single model. Our system outperforms all other submissions in the LLM track by 2.8 percent points and all submissions in the unconstrained track by 9.5 percent points. Furthermore, we perform a series of ablation experiments with different model sizes, empty node prediction methods, and cross-lingual zero-shot evaluation. The source code and the trained models are publicly available at https://github.com/ufal/crac2026-corpipe.

CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution

We present our submission to the LLM track of the 2026 Computational Models of Reference, Anaphora and Coreference (CRAC 2026) shared task. With an average CoNLL F1 score of 74.32 on the official test set, our system ranked first in the LLM track, and third overall. Our system is based on the Gemma-3-27b model, fine-tuned using a two-stage strategy with a multilingual base adapter followed by dataset-specific adapters. We represent mention spans by their headword using an XML-inspired format with local reindexing and annotate documents iteratively. These design choices proved effective across languages, document lengths, and annotation guidelines.

Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution

This paper describes the fifth edition of the Shared Task on Multilingual Coreference Resolution, held in conjunction with the CODI-CRAC 2026 workshop. Building on previous iterations, the task required participants to develop systems capable of mention identification and identity-based coreference clustering.
The 2026 edition specifically emphasizes long-range entities, defined as coreferential chains spanning significant distances, across many words and sentences.
The task expanded its linguistic scope by incorporating five new datasets and two additional languages. These additions leverage version 1.4 of CorefUD, a harmonized multilingual collection comprising 27 datasets in 19 languages.
In total, ten systems participated, including four LLM-based approaches (three fine-tuned models and one few-shot approach). While traditional systems still maintained their lead, LLMs demonstrated significant potential, suggesting they may soon challenge established approaches in future editions.

Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities

Evaluating pragmatic reasoning in large language models (LLMs) remains challenging because model behavior can vary depending on evaluation methods. Previous studies suggest that prompt-based judgments may diverge from models' internal probability distributions, raising questions about whether observed performance reflects underlying competence or task-induced behavior. This study examines this issue using scalar diversity as a graded diagnostic for pragmatic inference. Following Hu & Levy (2023), this study compares direct probability measurement and metalinguistic prompting across multiple models and experimental settings. The results show that neither evaluation method consistently outperforms the other and that pragmatic behavior varies substantially across model families, prompting strategies, and task structures. Moreover, scalar diversity gradients emerge only in specific model–condition combinations, suggesting that pragmatic reasoning in LLMs reflects an interaction between internal probabilistic representations and task-induced prompting behavior rather than a stable competence captured by a single evaluation paradigm. These findings highlight the central role of evaluation design in interpreting pragmatic abilities in LLMs.

Evaluating Pragmatic Reasoning in Large Language Models: Evidence from Scalar Diversity

Human users increasingly communicate with large language models (LLMs), but LLMs suffer from frequent overconfidence in their output, even when its accuracy is questionable, which undermines their trustworthiness and perceived legitimacy. Therefore, there is a need for language models to signal their confidence in order to reap the benefits of human-machine collaboration and mitigate potential harms. Verbalized uncertainty is the expression of confidence with linguistic means, an approach that integrates perfectly into language-based interfaces. Most recent research in natural language processing (NLP) overlooks the nuances surrounding human uncertainty communication and the biases that influence the communication of and with machines. We argue for anthropomimetic uncertainty, the principle that intuitive and trustworthy uncertainty communication requires a degree of imitation of human linguistic behaviors. We present a thorough overview of the research in human uncertainty communication, survey ongoing research in NLP, and perform additional analyses to demonstrate so-far underexplored biases in verbalized uncertainty. We conclude by pointing out unique factors in human-machine uncertainty and outlining future research directions towards implementing anthropomimetic uncertainty.

Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing

Previous work examining the Uniform Information Density (UID) hypothesis has shown that while information as measured by surprisal metrics is distributed more or less evenly across documents overall, local discrepancies can arise due to functional pressures corresponding to syntactic and discourse structural constraints. However, work thus far has largely disregarded the relative salience of discourse participants. We fill this gap by studying how overall salience of entities in discourse relates to surprisal using 70K manually annotated mentions across 16 genres of English and a novel minimal-pair prompting method. Our results show that globally salient entities exhibit significantly higher surprisal than non-salient ones, even controlling for position, length, and nesting confounds. Moreover, salient entities systematically reduce surprisal for surrounding content when used as prompts, enhancing document-level predictability. This effect varies by genre, appearing strongest in topic-coherent texts and weakest in conversational contexts. Our findings refine the UID competing pressures framework by identifying global entity salience as a mechanism shaping information distribution in discourse.

Expect the Unexpected? Testing the Surprisal of Salient Entities

Multi-domain Dialogue State Tracking (DST) requires discourse coherence that transcends independent slot-filling. Most existing approaches rely on statistical regularities within static schemas, failing to capture the semantic coordination semantics governing simultaneous slot updates. In this paper, we propose Event-DST, which models latent events as cognitive organizing units to dynamically coordinate slot interactions. By projecting dialogue context into a continuous semantic space, our model induces a dynamic structural bias to enforce pragmatic consistency. This structural guidance is integrated via a dual-stream fusion strategy that balances top-down structural constraints with bottom-up textual precision. Experimental results on two benchmarks demonstrate the superiority of our framework, providing an interpretable and parameter-efficient path toward robust dialogue understanding.

Event-Guided Slot Interaction for Multi-Domain Dialogue State Tracking

Logical Reasoning is a novel approach to deal with challenging Machine Reading Comprehension tasks by utilizing the ability to construct logical structures in natural language. However, previous promising studies struggle with the accuracy of logical unit division and the consistency of model prediction on equivalent semantics. In this paper, we propose ThinkStruct, a new method that leverages a transformer network enhanced with the information of Rhetorical Structure Theory relations for logical reasoning. Specifically, our method uses the Rhetorical Structure Theory to split natural language text into Elementary Discourse Units (EDUs) and identify the relationship among these units. Node information is then fed into the fully connected transformer network, which is enhanced with logical relationships among the extracted units via adjacency matrix. Subsequently, the features of the transformer network are integrated before being passed into the answer prediction module. In addition, we employ a contrastive learning module for improving its understanding of the relationship between Elementary Discourse Units. Our experiments on the LogiQA and Reclor datasets demonstrate that our results outperform other state-of-the-art models.

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Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution

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