United States

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.

ACL 2026

A First Step towards Dialog Simulation with Grounded Dialog Graphs

information-seeking dialog

dialog simulation

synthetic data

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

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workshop paper

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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.

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.

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

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