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Errors in automatic coreference resolution can be traced back to errors in mention detection and coreference linking. In this paper, we analyse the errors in mention detection produced by the coreference resolver CorPipe (Straka 2023). In particular, we evaluate the performance on different variants of German (written, spoken, original, and simplified). We discuss the errors against the background of the fact that the tool was trained on a combination of different coreference corpora, including two German datasets with partially conflicting annotation guidelines. The results indicate that simplification has a significant effect on mention detection independent of the modality.

ACL 2026

Errors in coreference resolution in German: Effects of modality, simplification and heterogeneous training data

simplified german

mention detection

error analysis

coreference resolution

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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.

We introduce BeDiscovER (Benchmark of Discourse Understanding in the Era of Reasoning Language Models), an up-to-date, comprehensive suite for evaluating the discourse-level knowledge of modern LLMs. BeDiscovER compiles 5 publicly available discourse tasks across discourse lexicon, (multi-)sentential, and documental levels, with in total 52 individual datasets. It covers both extensively studied tasks such as discourse parsing and temporal relation extraction, as well as some novel challenges such as discourse particle disambiguation (e.g., “just”), and also aggregates a shared-task on Discourse Relation Parsing and Tree-banking for multilingual and multi-framework discourse relation classification. We evaluate open-source LLMs: Qwen3 series, DeepSeek-R1, and frontier reasoning model GPT-5-mini on BeDiscovER, and find that state-of-the-art models exhibits strong performance in arithmetic aspect of temporal reasoning, but they struggle with long-dependency reasoning and some subtle semantic and discourse phenomena, such as rhetorical relation classification.

BeDiscovER: The Benchmark of Discourse Understanding in the Era of Reasoning Language Models

This paper describes our multilingual coreference system developed for the CRAC 2026 unconstrained track. We introduce a unified, single-model architecture based on Conditional Random Fields (CRFs) that supports 20 languages. Notably, our approach achieves multilingual resolution without the use of large language models (LLMs) or pretrained weights. In contrast to resource-intensive neural methods, the proposed model is efficient, and suitable for deployment on standard hardware (CPUs). It uses linguistic and contextual features to capture coreference relations across languages with diverse syntactic and morphological properties. Model training was conducted using the official data distributions released for the CRAC 2026 shared task. This methodology provides a robust, scalable solution for multilingual NLP, demonstrating high utility within resource-constrained environments. The results highlight that feature-driven structured models remain effective for complex cross-lingual tasks. The performance on test data is similar to the results obtained for the development data.

Lightweight Multilingual Coreference Resolution without LLMs @CRAC2026

This paper presents Landcore (LANguage-Dependent COference REsolution), our submission to the LLM Track of the CRAC 2026 Shared Task on Multilingual Coreference Resolution. We explore the capabilities of LLMs in coreference resolution across multiple languages and domains, using a~few-shot prompting approach. We design a~comprehensive prompt that includes detailed instructions and examples and further enhance it using an LLM to produce language-specific prompts. We present an EML annotation scheme that is more suitable for LLMs than the provided formats. Although our solution is not the best-performing, we show that our ideas improve performance across various settings.

Landcore: Coreference Resolution with Language-Specific LLM-Enhanced Prompts and XML-Inspired Annotation Scheme

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.

Generative Multilingual Coreference Resolution at CRAC 2026

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.

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Coreference in simplified German: Linguistic features and challenges of automatic annotation

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