United States

Part-of-speech (POS) tagging for Medieval Romance languages remains challenging due to orthographic variation, morphological complexity, and limited annotated resources. This paper presents a systematic empirical evaluation of large language models (LLMs) for POS tagging across three medieval varieties: Medieval Occitan, Medieval Catalan, and Medieval French. We compare traditional rule-based and statistical taggers with modern open-source LLMs under zero-shot prompting, few-shot prompting, monolingual fine-tuning, and cross-lingual transfer learning settings.

Experiments on historically grounded datasets show that LLM-based approaches consistently outperform traditional taggers, with fine-tuning and multilingual training yielding the largest improvements. In particular, cross-lingual transfer learning substantially benefits under-resourced varieties, while targeted bilingual training can outperform broader multilingual configurations for specific target languages. The results highlight the importance of linguistic proximity and dataset characteristics when designing transfer strategies for historical NLP.

These findings provide empirical insights into the applicability of modern neural methods to medieval text processing and provide practical guidance for deploying LLM-based POS tagging pipelines in digital humanities research. All code, models, and processed datasets are released for reproducibility.

ACL 2026

Unlocking Medieval Texts: How Large Language Models Transform POS Tagging for Historical Romance Languages

medieval romance languages

digital humanities

low-resource languages

pos tagging

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for 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.

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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 computational framework for evaluating structural properties of the undeciphered Indus script. The study uses a corpus of approximately 6,100-6,500 inscriptions. The analytical approach combines unsupervised visual clustering of sign morphology, entropy-based sequence analysis, Kullback-Leibler divergence comparison, and neural sequence modeling (BiLSTM). The results indicate directional asymmetry and structured combinatorial patterns in sign sequences. We conclude that the Indus sign sequences exhibit statistical properties consistent with structured symbolic systems and not easily explained by random generation.

Statistical Structure in Indus Sign Sequences

We present the result of preliminary explorations of using the topology of embedded manifolds as a semantic invariant. Our main question is whether the topology of large embedded corpora is invariant in the following two senses. First, one might reasonably expect that the same corpus in two languages would give topologically equivalent embeddings. Second, one might reasonably expect that the same corpus embedded by two different embedding models might give topologically equivalent embeddings. In the paper we will justify these intuitions and give preliminary results indicating that they are, to some extent, justified.

Exploring Topological Invariance in Semantic Embeddings

Automated Essay Scoring (AES) is shifting from feature-engineering to LLMs, yet current training-free approaches struggle with calibration, often exhibiting a "middle-score bias" that fails to distinguish between exceptional and weak writings. In this work, we introduce MADRAG (Multi-Agent Debate with Retrieval-Augmented Generation), a training-free framework designed to achieve the reliability of supervised models without the need for labeled training data. MADRAG decomposes the scoring process into a multi-agent interaction: an Advocate highlights essay strengths, a Skeptic critiques weaknesses, and a Judge synthesizes these arguments to assign a score. Crucially, we augment the Judge with RAG mechanism that retrieves rubric-aligned exemplar essays spanning the full score range, grounding the debate in concrete evidence. Evaluating our approach on the ASAP dataset for analytic trait scoring, we demonstrate that MADRAG significantly outperforms existing prompt-based LLM baselines and achieves performance competitive with state-of-the-art supervised models.

From Advocacy to Judgment: Training-Free Analytic Essay Scoring with Multi-Agent Debate and Exemplar Retrieval

Research on online cultural production shows that platforms are acting as mediators that can heavily shape textual form. Yet, empirical work is often platform-bounded, making it difficult to assess whether stylistic regularities that we observe are indeed genre signals or if some of them are platform artefacts. We address this question through a cross-platform design focused on \textit{creepypasta}, a digital-born horror genre circulating across heterogeneous infrastructures. Using a corpus of $\sim$23,000 English-language stories published from 2007 to 2024 on Reddit's \texttt{/r/nosleep} and the Creepypasta Fandom wiki, we compare stylistic profiles across platforms and relate them to differences in rule regimes and moderation practices, established through qualitative extraction and close reading of platform guidelines. Across readability indices, lexical diversity measures, syntactic proxies, and a cross-fit feature-based model, we find that platform membership leaves only a narrow stylistic imprint, largely reducible to a single architectural rule: r/NoSleep's mandatory first-person narration. Beyond this constraint, differences are modest and fail to form coherent platform-specific stylistic signatures. This helps us define what is stylistically common in creepypastas, and understand what the genre is to its writers beyond the topics it deals with or the platform it is written on.

Never Care For What They Say ? Platform vs Genre Rules in Online Horror Narratives (2007--2024)

This study proposes and tests loudness standard
deviation (σ) of fictional sound events as an
acoustically grounded proxy for detecting ex-
plicit content in romance fiction. Working with
a subcorpus of novels from the Harlequin Men
Made in America series, scenes were annotated
for character and ambient sound with loudness
levels. Additionally, the scenes were annotated
on a ternary severity scale with two content ad-
visory categories drawn from the PG-STORY
taxonomy by (Tsai et al.) (SEX & NUDITY and
VIOLENCE & SCARINESS), and tested whether
within-scene loudness σof character and ambi-
ent sound correlates with either category. Loud-
ness standard deviation analyses of character
and ambient sounds in scenes featuring explicit
content reveal that erotic scenes are acousti-
cally marked by significantly higher variability
in character-produced sounds, reflecting the dy-
namic range from whispered dialogue to vocal-
ized arousal.

Between Whispers and Screams: Loudness Standard Deviation as a Proxy for Explicit Content Detection in US Romance Novels

This study introduces and analyzes a novel authorship attribution case: the children’s stories published by Oscar and Constance Wilde. We analyzed the corpus of stories with both supervised (SVM with string kernel) and unsupervised (Hierarchical Clustering via Rank Distance) methods and found a strong stylistic similarity between the story "The Selfish Giant" published by Oscar Wilde and the stylometric profile of Constance Wilde. Starting from this baseline, we also explored the the capabilities of LLMs in authorship attribution via Perplexity. Our finding suggests that the story "The Selfish Giant" might be the result of a collaboration between Oscar and Constance Wilde. Moreover, our results pointed to the distinct stylistic fingerprints of the two authors with regards to the rest of the corpus, confirming that their respective styles are separable despite shared genre and period.

Computational Authorship Attribution in the Children’s Tales of Oscar and Constance Wilde: The Case of "The Selfish Giant"

This work investigates the extent to which open-source Large Language Models (LLMs) can improve accessibility of unstructured historical documents by performing abstractive summarization and Named Entity Recognition (NER). We evaluate seven open-source LLMs in zero-shot settings and apply these tasks to witness testimonies collected by the South African Truth and Reconciliation Commission (TRC), which archived a large body of text documenting human rights violations during apartheid. Despite their historical significance, these texts are difficult to access due to their length, lack of standardized structure, and the absence of systematic indexing. Open-source LLMs show strong performance in summarization, with most models surpassing non-LLM baselines (maximum BERTScore 0.77), while NER performance remains limited (maximum F1-score 0.61). Results suggest a trade-off in which stylistic fluency is prioritized over factual precision. A two-stage pipeline, summarization followed by NER on LLM summaries, leads to measurable improvements.

Evaluating Open-Source LLMs for Text Summarization and Named Entity Recognition in Long, Unstructured Text

This paper introduces \textit{Perspectives}, an interactive extension of a qualitative data analysis tool suite developed at our university, designed to empower Digital Humanities (DH) scholars to explore and organize large, unstructured document collections. \textit{Perspectives} implements a flexible, aspect-focused document clustering pipeline with human-in-the-loop refinement capabilities.
We showcase how this process can be initially steered by defining analytical lenses through document rewriting prompts and instruction-based embeddings, and further aligned with user intent through tools for refining clusters and mechanisms for fine-tuning the embedding model. The demonstration highlights a typical workflow, illustrating how DH researchers can leverage \textit{Perspectives}'s interactive document map to uncover topics, sentiments, or other relevant categories, thereby gaining insights and preparing their data for subsequent in-depth analysis.

Perspectives -- Interactive Document Clustering for Qualitative Data Analysis

Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have enabled diverse retrieval methods. However, existing retrieval methods often fail to accurately retrieve results for negation and exclusion queries. To address this limitation, prior approaches rely on embedding adaptation or fine-tuning, which introduce additional computational cost and deployment complexity. We propose Direct Embedding Optimization (DEO), a training-free method for negation-aware text and multimodal retrieval. DEO decomposes queries into positive and negative components and optimizes the query embedding with a contrastive objective. Without additional training data or model updates, DEO outperforms baselines on NegConstraint, with gains of +0.0738 nDCG@10 and +0.1028 MAP@100, while improving Recall@5 by +6\% over OpenAI CLIP in multimodal retrieval. These results demonstrate the practicality of DEO for negation- and exclusion-aware retrieval in real-world settings. The code is publicly available at GitHub.

DEO: Training-Free Direct Embedding Optimization for Negation-Aware Retrieval

The rapid growth of scientific publications makes it increasingly difficult to track and synthesize research progress. While Large Language Models (LLMs) can support automated survey generation, existing methods retrieve unstructured data and require models to infer conceptual, methodological, and taxonomic relations from raw text at generation time. We introduce StructSurvey, a hierarchical multi-agent framework that shifts structural reasoning from generation to retrieval by dynamically constructing graph-based representations of entities, relations, and topical taxonomies. We evaluate StructSurvey on a new reference-grounded benchmark of ACL survey papers for reproducible long-form scientific summarization. Compared with embedding-only retrieval baselines, StructSurvey improves ROUGE-1 recall by +2.9 and ROUGE-2 recall by +1.0 on average, without reducing precision. It also improves LLM-as-a-Judge ratings for logical structure, depth, and synthesis, showing that explicit structural retrieval yields surveys closer to human-written organization and reasoning.

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