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Crowd-sourcing implicit discourse relations has been shown to yield a variety of plausible interpretations, but also to be prone to annotation errors. In this study, we apply the dataset cartography technique (Swayamdipta et al., 2020) to implicit discourse relations. We find that error identification via low confidence is infeasible for IDRR datasets; 
however, high confidence in models coincides with strong textual heuristics and with high item-wise agreement, pointing to shortcut use by models and, partly, by crowd workers. Balancing out this factor is required to ensure robust IDRR learning and evaluation.

ACL 2026

Dataset Cartography for IDRR: Promises and Pitfalls

discourse annotation

dataset cartography

training dynamics

crowdsourcing

discourse parsing

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

workshop paper

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. 
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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.

This paper introduces a novel 'universal' approach to discourse annotation, serving as a comprehensive synthesis of the ISO 24617-8 semantic annotation framework and a newly developed multi-layer model of coherence relations. To address the complexities of text analysis, we present a hierarchical classification and a systematic decision tree. By unifying disparate formalisms, our model provides researchers with a robust, standardised methodology for analysing complex discourse structures across various linguistic contexts.

Universal Discourse Relations: A Proposal

This abstract describes a work-in-progress study of conversation-level effects on in-context word meaning. The abstract describes a definition modeling-based metric for lexical semantic conversational contingency and presents the results of a pilot study using the Fora corpus.

Investigating discourse-level lexical semantic adaptation: A definition modeling approach

In this work, we propose a method for dialog simulation to gather high-quality open-domain, multi-turn question answering conversations. The simulation is grounded on Stack Exchange posts and motivated by computational discourse theory. We first convert forum posts into structured directed graphs; then, different traversals through the graph represent possible conversational trajectories. Our proposed graph traversal algorithm produces dialogs optimized for conversational efficiency. In addition, we propose an evaluation framework based on Gricean conversational maxims. Expert-level human annotators evaluate transcripts according to our framework; dialogs produced by our method receive ratings that are competitive with dialogs from prior work.

A First Step towards Dialog Simulation with Grounded Dialog Graphs

This extended abstract presents a Rational Speech-Act model predicting the persuasiveness ("argument strength") of explanations, as well as preliminary experimental data that validates the predictions of the model. While first-order Bayesian models developed by psychologists of reasoning generally predict that arguments become stronger when more reasons are provided, we investigate the competing intuition that excess reasons can weaken an argument ("the lady doth protest too much!"). We account for this effect by drawing upon a growing body of literature which shows that listeners make pragmatic inferences to rationalize violations of their "informativity expectations".

The lady doth protest too much: discourse expectations affect judgments of argument strength

We present semantic-pragmatic specification and annotation (ellipsis, coreference, bridging and discourse relations, information structure, scope of negation) in the multi-layer, genre-diversified, 3+ million-token Enriched PDT. While morphology and syntax work almost exclusively on sentence level, the semantic-pragmatic phenomena are often related to two or more neighbouring sentences and possibly to an extra-linguistic context. In the contribution, we describe these phenomena from both the linguistic perspective (form of expression, relation to syntax and morphology) and the cognitive perspective (relation to context, real world knowledge, as well as to the related processes such as thinking or reasoning) – classifying the possible relations between the semantic-pragmatic units into cognitively plausible, distinguishable, and human-understandable categories. We have applied our results to the corpus, by annotating it in its entirety. The resulting Enriched PDT is publicly and freely available, to serve for verification and further investigation of (not only) these phenomena.

Semantic-pragmatic Annotations in the Prague Dependency Treebank

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.

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

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.

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