GenAI4ELab/papercli-papers huggingface dataset:
Where Frameworks (Dis)agree: A Study of Discourse Segmentation
Authors: Maciej Ogrodniczuk; Anna Latusek; Karolina Saputa; Alina Wróblewska; Daniel Ziembicki; Bartosz Żuk; Martyna Lewandowska; Adam Okrasiński; Paulina Rosalska; Anna Śliwicka; Aleksandra Tomaszewska; Sebastian Żurowski
Venue: EMNLP 2025 (Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025))

Abstract: This study addresses the fundamental task of discourse unit detection – the critical initial step in discourse parsing. We analyze how various discourse frameworks conceptualize and structure discourse units, with a focus on their underlying taxonomies and theoretical assumptions. While approaches to discourse segmentation vary considerably, the extent to which these conceptual divergences influence practical implementations remains insufficiently studied. To address this gap, we investigate similarities and differences in segmentation across several English datasets, segmented and annotated according to distinct discourse frameworks, using a simple, rule-based heuristics. We evaluate the effectiveness of rules with respect to gold-standard segmentation, while also checking variability and cross-framework generalizability. Additionally, we conduct a manual comparison of a sample of rule-based segmentation outputs against benchmark segmentation, identifying points of convergence and divergence.Our findings indicate that discourse frameworks align strongly at the level of segmentation: particular clauses consistently serve as the primary boundaries of discourse units. Discrepancies arise mainly in the treatment of other structures, such as adpositional phrases, appositions, interjections, and parenthesised text segments, which are inconsistently marked as separate discourse units across formalisms.
Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser
Authors: Elena Chistova
Venue: EMNLP 2025 (Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025))

Abstract: We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and evaluate two training strategies: Multi-Head, which assigns separate relation classification layer per inventory, and Masked-Union, which enables shared parameter training through selective label masking. We first benchmark mono-treebank parsing with a simple yet effective augmentation technique for low-resource settings. We then train a unified model and show that (1) the parameter efficient Masked-Union approach is also the strongest, and (2) UniRST outperforms 16 of 18 mono‐treebank baselines, demonstrating the advantages of a single-model, multilingual end-to-end discourse parsing across diverse resources.
Corpus-Oriented Stance Target Extraction
Authors: Benjamin Steel; Derek Ruths
Venue: EMNLP 2025 (Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025))

Abstract: Understanding public discourse through the frame of stance detection requires effective extraction of issues of discussion, or stance targets. Yet current approaches to stance target extraction are limited, only focusing on a single document to single stance target mapping. We propose a broader view of stance target extraction, which we call corpus-oriented stance target extraction. This approach considers that documents have multiple stance targets, those stance targets are hierarchical in nature, and document stance targets should not be considered in isolation of other documents in a corpus. We develop a formalization and metrics for this task, propose a new method to address this task, and show its improvement over previous methods using supervised and unsupervised metrics, and human evaluation tasks. Finally, we demonstrate its utility in a case study, showcasing its ability to aid in reliably surfacing key issues of discussion in large-scale corpuses.
Information-Theoretic and Prompt-Based Evaluation of Discourse Connective Edits in Instructional Text Revisions
Authors: Berfin Aktas; Michael Roth
Venue: EMNLP 2025 (Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025))

Abstract: We present a dataset of text revisions involving the deletion or replacement of discourse connectives. Manual annotation of a replacement subset reveals that only 19% of edits were judged either necessary or should be left unchanged, with the rest appearing optional. Surprisal metrics from GPT-2 token probabilities and prompt-based predictions from GPT-4.1 correlate with these judgments, particularly in such clear cases.
Referential ambiguity and clarification requests: comparing human and LLM behaviour
Authors: Chris Madge; Matthew Purver; Massimo Poesio
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: In this work we examine LLMs’ ability to ask clarification questions in task-oriented dialogues that follow the asynchronous instruction-giver/instruction-follower format. We present a new corpus that combines two existing annotations of the Minecraft Dialogue Corpus — one for reference and ambiguity in reference, and one for SDRT including clarifications — into a single common format providing the necessary information to experiment with clarifications and their relation to ambiguity. With this corpus we compare LLM actions with original human-generated clarification questions, examining how both humans and LLMs act in the case of ambiguity. We find that there is only a weak link between ambiguity and humans producing clarification questions in these dialogues, and low correlation between humans and LLMs. Humans hardly ever produce clarification questions for referential ambiguity, but often do so for task-based uncertainty. Conversely, LLMs produce more clarification questions for referential ambiguity, but less so for task uncertainty. We question if LLMs’ ability to ask clarification questions is predicated on their recent ability to simulate reasoning, and test this with different reasoning approaches, finding that reasoning does appear to increase question frequency and relevancy.
Coreference in simplified German: Linguistic features and challenges of automatic annotation
Authors: Sarah Jablotschkin; Ekaterina Lapshinova-Koltunski; Heike Zinsmeister
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: In this paper, we analyse coreference annotation of the German language, focussing on the phenomenon of simplification, that is, the tendency to use words and constructions that are assumed to be easier perceived, understood, or produced. Simplification is one of the tools used by language users in order to optimise communication effectively. We are interested in how simplification is reflected in coreference in two different language products exposed to the phenomena of simplification: simultaneous interpreting and Easy German. For this, we automatically annotate simplified texts with coreference. We then evaluate the outputs of automatic annotation. In addition, we also look into quantitative distributions of some coreference features. Our findings show that although the language products under analysis diverge in terms of simplification driving factors, they share some specific coreference features. We also show that this specificity may cause annotation errors in simplified language, e.g. in non-nominal or split antecedents.
Revisiting the Givenness Hierarchy. A Corpus-Based Evaluation
Authors: Christian Chiarcos
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: 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.
Mention detection with LLMs in pair-programming dialogue
Authors: Cecilia Domingo; Paul Piwek; Svetlana Stoyanchev; Michel Wermelinger
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: 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.
The Elephant in the Coreference Room: Resolving Coreference in Full-Length French Fiction Works
Authors: Antoine Bourgois; Thierry Poibeau
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: 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.
Towards Adding Arabic to CorefUD
Authors: Dima Taji; Daniel Zeman
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: 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.
Exploring Coreference Resolution in Glosses of German Sign Language
Authors: Yuzheng Bao; Haixia Chai
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: 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.
Impact of ASR Transcriptions on French Spoken Coreference Resolution
Authors: Kirill Milintsevich
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: 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.
Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?
Authors: Michal Novák; Miloslav Konopik; Anna Nedoluzhko; Martin Popel; Ondrej Prazak; Jakub Sido; Milan Straka; Zdeněk Žabokrtský; Daniel Zeman
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: 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.
GLaRef@CRAC2025: Should we transform coreference resolution into a text generation task?
Authors: Olga Seminck; Antoine Bourgois; Yoann Dupont; Mathieu Dehouck; Marine Delaborde
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: We present the submissions of our team to the Unconstrained and LLM tracks of the Computational Models of Reference, Anaphora and Coreference (CRAC2025) shared task, where we ended respectively in the fifth and the first place, but nevertheless with similar scores: average CoNLL-F1 scores of 61.57 and 62.96 on the test set, but with very large differences in computational cost. Indeed, the classical pair-wise resolution system submitted to the Unconstrained track obtained similar performance but with less than 10% of the computational cost. Reflecting on this fact, we point out problems that we ran into using generative AI to perform coreference resolution. We explain how the framework of text generation stands in the way of a reliable text-global coreference representation. Nonetheless, we realize there are many potential improvements of our LLM-system; we discuss them at the end of this article.
CorPipe at CRAC 2025: Evaluating Multilingual Encoders for Multilingual Coreference Resolution
Authors: Milan Straka
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: 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.
Fine-Tuned Llama for Multilingual Text-to-Text Coreference Resolution
Authors: Jakub Hejman; Ondrej Prazak; Miloslav Konopík
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: This paper describes our approach to the CRAC 2025 Shared Task on Multilingual Coreference Resolution. We compete in the LLM track, where the systems are limited to generative text-to-text approaches. Our system is based on Llama 3.1-8B, fine-tuned to tag the document with coreference annotations. We have made one significant modification to the text format provided by the organizers: The model relies on the syntactic head for mention span representation. Additionally, we use joint pre-training, and we train the model to generate empty nodes. We provide an in-depth analysis of the performance of our models, which reveals several implementation problems. Although our system ended up in last place, we achieved the best performance on 10 datasets out of 22 within the track. By fixing the discovered problems in the post-evaluation phase, we improved our results substantially, outperforming all the systems in the LLM track and even some unconstrained track systems.
Few-Shot Coreference Resolution with Semantic Difficulty Metrics and In-Context Learning
Authors: Nguyen Xuan Phuc; Dang Van Thin
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: This paper presents our submission to the CRAC 2025 Shared Task on Multilingual Coreference Resolution in the LLM track. We propose a prompt-based few-shot coreference resolution system where the final inference is performed by Grok-3 using in-context learning. The core of our methodology is a difficulty- aware sample selection pipeline that leverages Gemini Flash 2.0 to compute semantic diffi- culty metrics, including mention dissimilarity and pronoun ambiguity. By identifying and selecting the most challenging training sam- ples for each language, we construct highly informative prompts to guide Grok-3 in predict- ing coreference chains and reconstructing zero anaphora. Our approach secured 3rd place in the CRAC 2025 shared task.
Few-Shot Multilingual Coreference Resolution Using Long-Context Large Language Models
Authors: Moiz Sajid; Seemab Latif; Zuhair Zafar; Muhammad Moazam Fraz
Venue: EMNLP 2025 (Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference)

Abstract: In this work, we present our system, which ranked second in the CRAC 2025 Shared Task on Multilingual Coreference Resolution (LLM Track). For multilingual coreference resolution, our system mainly uses long-context large language models (LLMs) in a few-shot in-context learning setting. Among the various approaches we explored, few-shot prompting proved to be the most effective, particularly due to the complexity of the task and the availability of high-quality data with referential relationships provided as part of the competition. We employed Gemini 2.5 Pro, one of the best available closed-source long-context LLMs at the time of submission. Our system achieved a CoNLL F1 score of 61.74 on the mini-testset, demonstrating that performance improves significantly with the number of few-shot examples provided, thanks to the model’s extended context window. While this approach comes with trade-offs in terms of inference cost and response latency, it highlights the potential of long-context LLMs for tackling multilingual coreference without task-specific fine-tuning. Although direct comparisons with traditional supervised systems are not straightforward, our findings provide valuable insights and open avenues for future work, particularly in expanding support for low-resource languages.
The DISRPT 2025 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification
Authors: Chloé Braud; Amir Zeldes; Chuyuan Li; Yang Janet Liu; Philippe Muller
Venue: EMNLP 2025 (Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025))

Abstract: In 2025, we held the fourth iteration of the DISRPT Shared Task (Discourse Relation Parsing and Treebanking) dedicated to discourse parsing across formalisms. Following the success of the 2019, 2021, and 2023 tasks on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification, this iteration added 13 new datasets, including three new languages (Czech, Polish, Nigerian Pidgin) and two new frameworks: the ISO framework and Enhanced Rhetorical Structure Theory, in addition to the previously included frameworks: RST, SDRT, DEP, and PDTB. In this paper, we review the data included in DISRPT 2025, which covers 39 datasets across 16 languages, survey and compare submitted systems, and report on system performance on each task for both treebanked and plain-tokenized versions of the data. The best systems obtain a mean accuracy of 71.19% for relation classification, a mean F1 of 91.57 (Treebanked Track) and 87.38 (Plain Track) for segmentation, and a mean F1 of 81.53 (Treebanked Track) and 79.92 (Plain Track) for connective identification. The data and trained models of several participants can be found at https://huggingface.co/multilingual-discourse-hub.