United States

Nearly all studies on web registers – online text varieties associated with distinct social contexts and linguistic features – use full documents as the unit of analysis. However, web documents often contain sections in different registers. A cooking blog post, for instance, may combine personal storytelling, recipe instructions, user comments, and promotional text within a single URL. This internal variation raises doubts about the validity of document-level register labeling. In this paper, we propose an LLM-based segmentation approach that identifies register-homogeneous segments within documents and apply it to a 10,000-document English sample from HPLT 3.0. We show that segmentation addresses persistent problems in register analysis, including low inter-annotator agreement and category fuzziness. Strikingly, it also reveals that most web documents contain more than one register, making register mixing the norm rather than the exception on the web.

ACL 2026

Register Mixing Is the Norm on the Web

register variation

text segmentation

corpus linguistics

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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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Identifying intertextual parallels is central to philology, traditionally requiring labor-intensive manual analysis. While digitized historical corpora enable automated approaches using semantic sentence embeddings, training such models requires large annotated datasets, which are scarce for low-resource languages. We address this challenge by introducing a scalable automatic annotation pipeline for training semantic embedding models for Classical Tibetan. Our method combines unsupervised contrastive bootstrapping with iterative pair mining, generating silver-standard similarity labels through two complementary annotation strategies: (1) an ensemble of embedding models and rerankers, and (2) an LLM-as-a-judge committee using best–worst scaling. 
When combined with a domain‑specific, gold‑standard annotated dataset for sequential fine‑tuning, the resulting text-similarity model achieves a state‑of‑the‑art Spearman correlation of 0.864 on the STS task. This enables effective semantic search in Classical Tibetan and provides a framework for automatic supervision in low-resource languages used in digital humanities. We will make our code, dataset, and trained model publicly available upon publication.

Scaling Sentence Similarity for Classical Tibetan with Automatic Annotations

Historical corpora for Tagalog remain limited, particularly texts produced during the Martial Law period under the dictatorship of Ferdinand Marcos Sr. (1972–1986). Much of this material remains undigitized, restricting computational analysis of a significant period in Philippine political history. To support research on historical Tagalog texts, we introduce PHMartialLawNER, a gold-standard named entity recognition corpus constructed from newspapers and underground publications of the Martial Law era. The corpus includes approximately 13k extracted sentence segments (362,000 tokens), consolidated into 8k annotated text spans through a semi-automatic pipeline with manual validation. The reliability of the annotation is measured using Cohen’s $\kappa$, reaching 0.86 on all tokens and 0.72 on annotated tokens, with a pairwise F1-score of 0.74. The schema defines historically relevant entity categories including Person (Individual, Collective), Organization (Political, Government, Other), Event (Local, International), Production (Media, Government, Doctrine), as well as Time, Numerical Statistics, Location, and Object entities, specifically identifying weapon artifacts. We establish baseline performance using GLiNER variants, calamanCy models, and transformer-based architectures under zero-shot and few-shot settings. The PHMartialLawNER corpus will be publicly released to support Tagalog NLP, historical text processing, and digital humanities research.

PHMartialLawNER: A Tagalog Named Entity Recognition Corpus for the Philippine Martial Law Era

Literary translation requires balancing target-language fluency with faithfulness to the source. Recent large language models (LLMs) often produce fluent translations, but it remains unclear whether fluency corresponds to semantic preservation in literary text. We examine this relationship using 130,486 translated paragraphs from 106 novels in 16 source languages, including human, Google Translate, and TranslateGemma translations. Fluency is measured as original-likeness with a translationese classifier trained on paragraph part-of-speech n-grams, and faithfulness with the automatic translation evaluation metric COMET-KIWI. We control for paragraph length and find a consistent negative correlation between fluency and faithfulness. The pattern appears for both human and Google Translate, but is weaker and often non-significant for TranslateGemma. These results show that segment length matters for automatic evaluation and suggest a tradeoff between fluency and faithfulness in literary translation.

Fluency and Faithfulness in Human and Machine Literary Translation

We investigate narrative agency in hu-
man–LLM creative co-writing, asking who
drives story development in turn-based collabo-
ration. Using a new corpus of human–LLM co-
written stories, we apply sentiment and seman-
tic modeling to quantify affective alignment
and semantic novelty in turn-taking, and direc-
tional measures to assess which agent shapes
narrative progression. Our results show asym-
metric influence: human turns introduce greater
semantic novelty and are more likely to shape
subsequent developments, whereas LLM con-
tributions predominantly elaborate on human-
introduced elements. At the sentiment level,
alignment is also asymmetric, but more bidirec-
tional: LLMs exhibit stronger turn-level emo-
tional adaptation than humans, but both agents
track each other’s emotional valence and LLMs
show an independent tendency to more pos-
itive emotional baselines. These findings in-
dicate a complementary division of labor in
human–LLM co-writing, where humans drive
narrative innovation and direction, while LLMs
act as adaptive amplifiers that sustain coherence
and elaborate emerging narratives.

Directional Alignment and Narrative Agency in Human–LLM Co-Writing

AI-driven language technologies are increasingly used in hiring, but they may encode and reproduce harmful social stereotypes. Prior work often studies bias mitigation methods in isolation and outside realistic application settings. We examine the combined effects of data-level and model-level debiasing in a hiring-related context, using Norwegian-language academic bios and a proxy STEM/non-STEM classification task. Specifically, we study masking sensitive information, GenWriter-based rewrites \citep{soundararajan2025genwriter}, and adversarial debiasing \citep{han2021diverse,han2022towards}. We evaluate these interventions using downstream task performance, group fairness metrics, intrinsic bias tests based on WEAT \citep{caliskan2017semantics}, and measures of gender leakage from hidden representations. We find that combining masking, GenWriter rewrites, and adversarial debiasing substantially reduces gender leakage while maintaining or improving downstream performance. However, effects on fairness gaps and intrinsic bias are mixed, underscoring the need for downstream, context-sensitive evaluation of bias mitigation methods in hiring-related NLP.

Bias Mitigation in Hiring-Related NLP: Interactions Between Masking, Rewriting, and Adversarial Debiasing

While digitized corpora have transformed the study of intellectual transmission, current methods rely heavily on lexical text reuse detection, capturing verbatim quotations but fundamentally missing paraphrases and complex implicit engagement. This paper evaluates semantic search in 18th-century intellectual history through the reception of John Locke’s foundational work. Using expert annotation grounded in a semantic taxonomy, we examine whether an off-the-shelf semantic search pipeline can surface meaning-level correspondences overlooked by lexical methods. Our results demonstrate that semantic search retrieves substantially more implicit receptions than lexical baselines. However, linguistic diagnostics also reveal a "lexical gatekeeping" effect, where retrieval remains partially constrained by surface vocabulary overlap. These findings highlight both the potential and the limitations of semantic retrieval for analyzing the circulation of ideas in large historical corpora.

Matching Meaning at Scale: Evaluating Semantic Search for 18th-Century Intellectual History through the Case of Locke

How did the thematic repertoire of early English-language science fiction change as the genre consolidated between 1818 and 1930? Using a corpus of 238 public-domain texts, we apply temporally binned latent Dirichlet allocation (LDA), comparing models with and without Authorless preprocessing (which probabilistically downweights author-specific vocabulary). Cross-period topic alignments exceed a permutation null baseline, indicating continuity in topic structure over time. Full-corpus LDA can produce comparable topic quality, but only temporal binning enables diachronic alignment; within the binned setting, Authorless reduces author concentration and modestly increases the share of thematic topics without materially reducing coherence. Four high-continuity topic chains -- centered on mobility, affect, planetary scale, and scientific knowledge -- suggest a shift from earlier romantic and speculative concerns toward more consolidated technoscientific forms. These chains generate interpretable hypotheses about the literary history of early science fiction, and the workflow supports diachronic analysis in small, author-skewed corpora.

Tracing Thematic Change in Early English-Language Science Fiction, 1818-1930

This study investigates whether a high-quality, 19-label named entity recogniser for medieval Latin charters can be constructed using only a few hundred annotated sentences. The authors introduce "semantic scaffolding," an innovation that utilizes richly descriptive English label phrases as prompts to activate latent multilingual knowledge within the model. This is paired with a custom span-based architecture utilizing XLM-ROBERTa-large, 4-head attention pooling to handle long property descriptions, and a hybrid loss system including Asymmetric Focal-Dice and InfoNCE contrastive terms. Results demonstrate that semantic scaffolding enables fine-tuned GLiNER to reach 80.8% overlap F1, while the custom architecture achieves 83.4% overlap F1 using only 298 training sentences. Significantly, the paper provides an empirical demonstration that domain-specific pre-training on medieval Latin offers no performance advantage once task-specific fine-tuning is applied. While the model excels at frequent categories like PER (95.7% F1) and LOC (93.5% F1), challenges persist for rare, position-dependent legal categories such as LEG (53.1% F1) and TRANS (52.6% F1).

Twenty’s Plenty: Semantic Scaffolding and Span Architecture for 19-Label NER in Medieval Latin Charters

Motherboard’s Mother Tongue is a computational linguistics and artistic research project that explores a Large Language Model’s (LLM) vocal production of glossolalia-like language. Glossolalia, colloquially known as ‘speaking in tongues,’ consists of the human production of seemingly unintelligible utterances. It is, by its nature, difficult to annotate accurately with linguistic features relevant for natural language. The glossolalia-producing system demonstrated here consists of the interaction of 1) a ‘nonsense’ linguistic corpus 2) a micro-controller based environmental data stream and 3) a fine-tuned LLM. While discussing some philosophical and artistic considerations of machinic glossolalia, we also address some methodological considerations for NLP. Using the artistic project as a case study, we argue that machinic glossolalia presents a ‘stress test’ that could inform some creative redirections of NLP methods and reconsiderations of definitions held by the subfield.

Artistic Interventions for NLP Annotation Challenges: The Stress Test of Machinic Glossolalia

This paper addresses a practical problem in computational literary history: retrieving adventure novels from a large digitized collection of French fiction where genre metadata are sparse and unreliable. We begin with supervised genre modeling based on a historically situated seed list of 101 adventure novels drawn from literary scholarship. We compare several classifiers and representations, and validate them against 364 independently labeled adventure novels from the Chapitres corpus. The best-performing model, HistGradientBoosting on mean paragraph embeddings, achieves strong external recall (81\%) despite the small training set. We then apply this model to the 12,176-novel Fictions litt\éraires de Gallica archive and refine the resulting candidate corpus through a graph-based post-processing step over a $k$-nearest-neighbor similarity graph. On the Chapitres benchmark, this graph correction produces negligible changes in retrieval performance, indicating that it is not a generally superior classifier. On Gallica, however, it yields a more cohesive and homogeneous candidate corpus and surfaces interpretable correction cases, including missed canonical adventure novels and excluded borderline texts. We therefore argue that graph-based correction is best understood not as a replacement for supervised classification, but as a heuristic for refining large, noisy archival corpora where exhaustive manual annotation is impossible.

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