United States

Annotation errors in hate speech corpora are often attributed to annotator disagreement or bias. This paper argues that a substantial and underexamined class of errors originates upstream, from structural weaknesses in annotation guidelines themselves. When guidelines fail to encode the linguistic and cultural properties of the target discourse, they make certain errors structurally inevitable regardless of annotator quality. Focusing on Arabic social media discourse, a challenging setting due to its dialect continuum, culturally embedded insult conventions, sarcasm-heavy pragmatics, and complex religious rhetoric, we identify three mechanisms through which guideline design produces systematic annotation errors: cultural misclassification, when culturally specific hostile expressions fall outside annotation categories; dialectal ambiguity, when lexical meanings shift across regional varieties; and annotation projection, when frameworks developed for English moderation are applied to Arabic without adequate adaptation. Using six illustrative case studies with attested Arabic examples, we show how these mechanisms produce recurrent misannotations in existing datasets. We propose a taxonomy of five guideline-induced error types, an explicit mapping from mechanisms to error types, and a practical four-stage diagnostic framework for dataset builders.

ACL 2026

Beyond Annotator Disagreement: Guideline-Induced Errors in Arabic Hate Speech Annotation

guidelines

arabic

annotation

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>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing')">Conference Handbook</button></html>
<br><br><br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to ACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.aclweb.org/registration/) first.

Registration Required

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

workshop paper

Accurate linguistic annotation is crucial for creating high-quality datasets in specialized domains, yet manual labeling is often slow, expensive, and inconsistent. We present a reproducible workflow for evaluating the effectiveness of large language models (LLMs) as annotators of domain-specific health misinformation on social media. Using a data set of 169 Instagram posts on seed oils, expert nutritionists provided gold-standard labels (71% positives), which we compared against the outputs of five open-source LLMs. We introduce a hierarchical error taxonomy that categorizes LLM misclassifications according to the direction, mechanism, and contributing factors of the error, providing interpretable insights into model failures. Our analysis reveals systematic error patterns, including misinterpretation of nuanced claims and overconfidence in predictions, highlighting conditions under which LLM annotations do not align with expert judgment. Although the data set is modest in size and exhibits class imbalance, it reflects real‑world distributions of nutrition‑related Instagram content and motivates the need for a careful evaluation of the robustness of the LLM annotation. This study has implications for the development of frameworks for automated LLM-based annotators in the health and nutrition domains, as well as LLM developers in general.

When LLMs Disagree with Human Experts: Understanding LLM Annotation Failures in Nutrition Misinformation through Hierarchical Error Analysis using Seed Oil Narratives

Large Language Models have demonstrated significant progress in solving mathematical word problems through techniques like Chain-of-Thought (CoT) prompting. However, recent research indicates that these models often rely on statistical regularities and surface-level patterns rather than true logical reasoning, leading to performance drops when faced with minor problem perturbations or irrelevant information. In this study, we introduce Math Discourse Bank (Math-DB), a novel discourse framework and annotated dataset designed to enhance LLM reasoning. Inspired by the Penn Discourse TreeBank (PDTB) and mathematics education research, Math-DB defines a hierarchy of discourse senses designed for quantitative reasoning, including categories such as Change, Combine, Compare, and Equalize. We applied this framework to the GSM-Symbolic dataset of 12,500 problems, yielding 47,815 sense-labeled discourse relations over 11,414 successfully-aligned instances (91.3\% pipeline yield). Our experiments demonstrate that incorporating Math-DB annotations into CoT prompts consistently improves LLM performance across various difficulty levels.

Math-DB: A Discourse Framework for Mathematical Word Problems to Enhance LLM Reasoning

Situation Entity (SE) segmentation identifies clause-like discourse units focusing on verb constellations. While SE segmentation has been applied to contemporary English as a subtask of SE annotation, systematic guidelines for syntactically ambiguous constructions remain underspecified. We present principled SE segmentation guidelines for contemporary and historical varieties of English and German. Our inter-annotator agreement studies on Late Modern English (1700–1900) and New High German (1650–1900) corpora demonstrate substantial agreement. Using the existing SitEnt corpus in contemporary English, we implement a new automatic segmenter based on XLM-RoBERTa. Our evaluation examines cross-variety and cross-lingual generalization, demonstrating challenges both for human annotation efforts and in transferring segmenters trained on contemporary English to historical varieties. Our code and data are publicly available at https://github.com/coling-unia/sitent-segmenter-law2026.

Cross-Linguistic Situation Entity Segmentation for Discourse Analysis in Diachronic English and German Text

This paper presents (i) UD-CHILDES-BG, a manually corrected Universal Dependencies treebank of Bulgarian child and child-directed speech, (ii) a quantitative and phenomenon-based evaluation of inter-annotator agreement on developmental data, and (iii) a systematic analysis of parser errors in this underrepresented domain. We manually correct 4,338 dependency parses (10% of the CHILDES-BG corpus), of which 14% are double-annotated. Inter-annotator agreement on UAS/LAS is 88.57/81.49 for child-directed speech (CDS) and 89.12/81.57 for child speech (CS). Parser performance on the manually corrected portion is 93.13/85.86 for CDS and 91.18/81.72 for CS, compared to a reported 93.37/90.21 on the test set of adult written language. Our analyses reveal that CDS and CS pose challenges for dependency annotation and parsing, particularly in discourse-related structures, which are less common in adult written language.

UD-CHILDES-BG: a dependency treebank of Bulgarian child and child-directed speech

Linguistic annotation tools that work well for non-Indic languages (e.g. English, German, Spanish, etc.) often fail with Indic scripts due to complex Unicode properties, including visual reordering of vowel matras, conjunct characters, and grapheme clusters spanning multiple code points. In this paper, we present a web-based annotation platform \textit{IndiAnn}, designed for low-resource Indic languages, which uses native browser Unicode rendering, offset-based storage that preserves grapheme clusters, and no forced tokenization in the user interface. The tool supports annotation for tasks such as part-of-speech (POS) tagging, named entity recognition (NER), dependency relation annotation, and semantic role labelling (SRL), that maintain correct character boundaries and enable seamless interoperability with standard NLP pipelines and tools. The framework is designed for Indic languages and has been tested on Telugu, Hindi, Tamil, Malayalam, Bengali, Odia, Marathi, and Kannada, with no script breakage during annotation. To the best of our knowledge, this is the first ever attempt at building a unified annotation framework (IndiAnn), which covers annotation for such varieties of key NLP tasks, having provision for eight Indic languages. The code repository is made publicly available\footnote{ \url{https://github.com/Lavadeep/INDIANN}}.

IndiAnn: An Annotation Platform for Low-Resource Indic Languages

Automated Essay Scoring (AES) fundamentally depends on high-quality annotated data, yet systematic approaches to developing annotation guidelines remain largely undocumented, especially for Arabic. We present a comprehensive methodology for trait-based Arabic AES annotation, applied to build a dataset of 7,859 essays by high school students annotated across seven writing traits, achieving substantial inter-annotator agreement (QWK: 0.66--0.75). Our methodology encompasses: (1) a seven-dimensional scoring framework grounded in Arabic linguistic and rhetorical conventions; (2) over 25 pages of Arabic-language guidelines with terminology unification, text-type-specific scoring descriptors, and annotated student examples; (3) a multi-stage training protocol that raised annotator agreement (QWK) from 0.58 to 0.71 before production began; and (4) quality assurance mechanisms, including dual annotation and supervisor adjudication. We release all materials publicly, providing both a validated foundation for Arabic AES research and a replicable template for annotation guideline development in other morphologically complex, under-resourced languages.

Designing Annotation Guidelines for Trait-Based Arabic Automated Essay Scoring: A Systematic Methodology

Benchmarks for long-form summaries (four or more sentences) generated by language models increasingly serve as gold-standard references for developing, evaluating, and comparing faithfulness-checking systems. As their influence grows, understanding the challenges of annotating faithfulness errors within long, discourse-rich summaries becomes critical. We revisit three benchmarks spanning diverse text types and contrasting annotation designs. Using a discourse-aware evaluation framework together with human auditing, we identify cases where benchmark labels may be unreliable. Manual verification shows that $3.4$\%-$5.4$\% of sentence-level labels warrant revision due to discourse-level inconsistencies that standard annotation procedures overlook. We introduce a taxonomy of five recurring annotation error types, propose revised labels, and show that correcting these cases leads to meaningful shifts in system rankings. We conclude with recommendations for future annotation practices.

Revisiting Faithfulness Annotations for Long-form Summaries

Although widely used in dialog act prediction and generation, the Switchboard Dialog Act (SwDA) corpus has performed poorly in models incorporating prosodic information because of misalignment between speech and text data. In this paper, we report our completion of the work begun in Chen et al. (2024) in addressing these misalignment issues with an improved SwDA corpus called RASwDA (Re-Aligned Switchboard Dialog Act Corpus). Now fully re-aligned and validated, RASwDA finally meets standards of accuracy that allow for classification models trained on it to exceed classification benchmarks set by models trained on other Switchboard subcorpora.

Completing and Validating the Re-Aligned Switchboard Dialog Act Corpus

Despite a long tradition of work on extractive summarization, which by nature aims to recover the most important propositions in a text, little work has been done on operationalizing graded proposition salience in naturally occurring data. In this paper, we adopt graded summarization-based salience as a metric from previous work on Salient Entity Extraction (SEE) and adapt it to quantify proposition salience. We define the annotation task, apply it to a small multi-genre dataset, evaluate agreement and carry out a preliminary study of the relationship between our metric and notions of discourse unit centrality in discourse parsing following Rhetorical Structure Theory (RST).

Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation

Off-the-shelf large language models (LLMs) are increasingly used to automate text annotation, yet their effectiveness remains underexplored for underrepresented languages and specialized domains where the class definition requires subtle expert understanding. We investigate LLM-based annotation for a novel legal NLP task: identifying the presence and sentiment of credibility assessments in asylum decision texts. We introduce RAB-Cred, a Danish text classification dataset featuring high-quality, expert annotations and valuable metadata such as annotator confidence and asylum case outcome. We benchmark 21 open-weight models and 30 system-user prompt combinations for this task, and systematically evaluate the effect of model and prompt choice for zero-shot and few-shot classification. We zoom in on the errors made by top-performing models and prompts, investigating error consistency across LLMs, inter-class confusion, correlation with human confidence and sample-wise difficulty and severity of LLM mistakes. Our results confirm the potential of LLMs for cost-effective labeling of asylum decisions, but highlight the imperfect and inconsistent nature of LLM annotators, and the need to look beyond the predictions of a single, arbitrarily chosen model. The RAB-Cred dataset and code are available at https://github.com/glhr/RAB-Cred

Downloads

Next from ACL 2026

When LLMs Disagree with Human Experts: Understanding LLM Annotation Failures in Nutrition Misinformation through Hierarchical Error Analysis using Seed Oil Narratives

Similar lecture

Designing Annotation Guidelines for Trait-Based Arabic Automated Essay Scoring: A Systematic Methodology

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2026

When LLMs Disagree with Human Experts: Understanding LLM Annotation Failures in Nutrition Misinformation through Hierarchical Error Analysis using Seed Oil Narratives

Similar lecture

Designing Annotation Guidelines for Trait-Based Arabic Automated Essay Scoring: A Systematic Methodology

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads