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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.

ACL 2026

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

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. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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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 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.

In Search of Lost Adventure Novels: Supervised Genre Retrieval and Corpus Refinement in Gallica

This study examines narratives in which students describe challenges they faced in higher education due to low socioeconomic (SES) backgrounds and the strategies they used to overcome them. Using computational text analysis, we operationalize three educational theories, Paulo Freire’s Critical Pedagogy, Urie Bronfenbrenner’s Ecological Systems Theory, and Pierre Bourdieu’s Theory of Capital and Habitus to analyze patterns in these narratives. To strengthen the theory-to-method connection, we incorporate temporal timeline extraction, identifying ordered event sequences and tracking how challenges and forms of capital evolve across a student’s posting history. This temporal lens links theoretical categories (barriers, supports, forms of capital) to when they occur, highlighting moments for timely interventions. By combining theory-driven features with temporal analysis, we evaluate the explanatory capacity of each framework and demonstrate how computational methods can quantitatively examine qualitative lived experience at scale, supporting interdisciplinary research on equity in education.

Computational Modeling of Educational Theory in Low-Socioeconomic Contexts

The diachronic evolution from Latin to the Romance languages involved a restructuring of the grammatical gender system from a tripartite configuration (masculine, feminine, neuter) to a bipartite one (masculine, feminine). In this work, we introduce an interpretable deep learning framework to investigate this phenomenon at both lexical and contextual levels. First, we show that conventional tokenization strategies are insufficiently robust for this low-resource historical setting, and that our proposed tokenizer improves performance over these baselines. At the lexical level, we evaluate the contribution of morphological features to grammatical gender prediction. Subsequently, at the contextual level, we analyze how different part-of-speech categories contribute to grammatical gender prediction.

Lost in Translation? Exploring the Shift in Grammatical Gender from Latin to Occitan

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.

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

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.

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In Search of Lost Adventure Novels: Supervised Genre Retrieval and Corpus Refinement in Gallica

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Next from ACL 2026

In Search of Lost Adventure Novels: Supervised Genre Retrieval and Corpus Refinement in Gallica

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads