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There are strong constraints on the structure of a possible lexicon. For example, the negative correlation between word frequency and length known as Zipf&#39;s law, and a negative correlation between word length and phonotactic complexity appear to hold across languages. While lexical trade-offs like these have been examined individually, it is unclear how they interact as a system. In this paper, we propose causal discovery as a method for identifying lexical biases and their interactions in a set of variables. We represent the lexicon as a causal model, and apply the Fast Causal Discovery algorithm (Spirtes et al., 1995) to identify both causal relationships between measured variables and the existence of possible unmeasured confounding variables. We apply this method to lexical data including measures of word length, frequency, phonotactic complexity, and morphological irregularity for 25 languages and find evidence of universal associations involving word length with a high likelihood of involving an unmeasured confounder, suggesting that additional variables need to be measured to determine how they are related. We also find evidence of variation across languages in relationships between the remaining variables, and suggest that given a larger dataset, causal discovery algorithms can be a useful tool in assessing the universality of lexical biases.

NAACL 2025

Unzipping the Causality of Zipf’s Law and Other Lexical Trade-offs

typology

lexicon

causal discovery

causal inference

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. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

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

There are strong constraints on the structure of a possible lexicon. For example, the negative correlation between word frequency and length known as Zipf's law, and a negative correlation between word length and phonotactic complexity appear to hold across languages. While lexical trade-offs like these have been examined individually, it is unclear how they interact as a system. In this paper, we propose causal discovery as a method for identifying lexical biases and their interactions in a set of variables. We represent the lexicon as a causal model, and apply the Fast Causal Discovery algorithm (Spirtes et al., 1995) to identify both causal relationships between measured variables and the existence of possible unmeasured confounding variables. We apply this method to lexical data including measures of word length, frequency, phonotactic complexity, and morphological irregularity for 25 languages and find evidence of universal associations involving word length with a high likelihood of involving an unmeasured confounder, suggesting that additional variables need to be measured to determine how they are related. We also find evidence of variation across languages in relationships between the remaining variables, and suggest that given a larger dataset, causal discovery algorithms can be a useful tool in assessing the universality of lexical biases.

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Although functional specialization in the brain - a phenomenon where different regions process different types of information - is well documented, we still lack precise mathematical methods with which to measure it. This work proposes a technique to quantify how brain regions respond to distinct categories of information. Using a topic encoding model, we identify brain regions that respond strongly to specific semantic categories while responding minimally to all others. We then use a language model to characterize the common themes across each region's preferred categories. Our technique successfully identifies previously known functionally selective regions and reveals consistent patterns across subjects while also highlighting new areas of high specialization worthy of further study.

Quantifying Semantic Functional Specialization in the Brain Using Encoding Models of Natural Language

Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in naturalistic language samples can be useful for early screening of AD dementia. However, the nature of language deficits often requires test administrators to use various speech elicitation techniques during spontaneous language assessments to obtain enough propositional utterances from dementia patients. This could lead to the ``observer's effect'' on the downstream analysis that has not been fully investigated. Our study seeks to quantify the influence of test administrators on linguistic features in dementia assessment with two English corpora the ``Cookie Theft'' picture description datasets collected at different locations and test administrators show different levels of administrator involvement. Our results show that the level of test administrator involvement significantly impacts observed linguistic features in patient speech. These results suggest that many of significant linguistic features in the downstream classification task may be partially attributable to differences in the test administration practices rather than solely to participants' cognitive status. The variations in test administrator behavior can lead to systematic biases in linguistic data, potentially confounding research outcomes and clinical assessments. Our study suggests that there is a need for a more standardized test administration protocol in the development of responsible clinical speech analytics frameworks.

``Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test

Existing discourse corpora annotated under different frameworks adopt distinct but somewhat related taxonomies of relations. How to integrate discourse frameworks has been an open research question. Previous studies on this topic are mainly theoretical, although such research is typically performed with the hope of benefiting computational applications. In this paper, we show how the proposal by Sanders et al. (2018) based on the Cognitive approach to Coherence Relations (CCR) (Sanders et al.,1992, 1993) can be used effectively to facilitate cross-framework discourse relation (DR) classification. To address the challenges of using predicted UDims for DR classification, we adopt the Bayesian learning framework based on Monte Carlo dropout (Gal and Ghahramani, 2016) to obtain more robust predictions. Data augmentation enabled by our proposed method yields strong performance (55.75 for RST and 55.01 for PDTB implicit DR classification in macro-averaged F1). We compare four model designs and analyze the experimental results from different perspectives. Our study shows an effective and cross-framework generalizable approach for DR classification, filling a gap in existing studies.

Cross-Framework Generalizable Discourse Relation Classification Through Cognitive Dimensions

Voice-based AI development faces unique challenges in processing both linguistic and paralinguistic information. This study compares how large audio-language models (LALMs) and humans integrate speaker characteristics during speech comprehension, asking whether LALMs process speaker-contextualized language in ways that parallel human cognitive mechanisms. We compared two LALMs' (Qwen2-Audio and Ultravox 0.5) processing patterns with human EEG responses. Using surprisal and entropy metrics from the models, we analyzed their sensitivity to speaker-content incongruency across social stereotype violations (e.g., a man claiming to regularly get manicures) and biological knowledge violations (e.g., a man claiming to be pregnant). Results revealed that Qwen2-Audio exhibited increased surprisal for speaker-incongruent content and its surprisal values significantly predicted human N400 responses, while Ultravox 0.5 showed limited sensitivity to speaker characteristics. Importantly, neither model replicated the human-like processing distinction between social violations (eliciting N400 effects) and biological violations (eliciting P600 effects). These findings reveal both the potential and limitations of current LALMs in processing speaker-contextualized language, and suggest differences in social-linguistic processing mechanisms between humans and LALMs.

Distinct social-linguistic processing between humans and large audio-language models: Evidence from model-brain alignment

Speech errors are a natural part of communication, yet they rarely lead to complete communicative failure because both speakers and comprehenders can detect and correct errors. Although prior research has examined error monitoring and correction in production and comprehension separately, integrated investigation of both systems has been impeded by the scarcity of parallel data. In this study, we present SPACER, a parallel dataset that captures how naturalistic speech errors are corrected by both speakers and comprehenders. We focus on single-word substitution errors extracted from the Switchboard speech corpus, accompanied by speaker's self-repairs and comprehenders' responses from an offline text-editing experiment. Our exploratory analysis suggests asymmetries in error correction strategies: speakers are more likely to repair errors that introduce greater semantic and phonemic deviations, whereas comprehenders tend to correct errors that are phonemically similar to more plausible alternatives or do not fit into prior contexts. Our dataset enables future research on the integrated approach of language production and comprehension.

SPACER: A Parallel Dataset of Speech Production And Comprehension of Error Repairs

Humans deal with temporary syntactic ambiguity all the time in incremental sentence processing. Sentences with temporary ambiguity that causes processing difficulties, often reflected by increase in reading time, are referred to as garden-path sentences. Garden-path theories of sentence processing attribute the increases in reading time to the reanalysis of the previously ambiguous syntactic structure to make it consistent with the new disambiguating text. It is unknown whether transformer-based language models successfully resolve the temporary ambiguity after encountering the disambiguating text. We investigated this question by analyzing completions generated from language models for a type of garden-path sentence with ambiguity between a complement clause interpretation and a relative clause interpretation. We found that larger language models are worse at resolving such ambiguity.

Are Larger Language Models Better at Disambiguation?

In cases of pervasive uncertainty, cognitive systems benefit from heuristics or committing to more general hypotheses. Here we have presented a hierarchical cognitive model of lexical processing that synthesizes advances in early rational cognitive models with modern-day neural architectures. Probabilities of higher-order categories derived from layers extracted from the middle layers of an encoder language model have predictive power in accounting for several reading measures for both predicted and unpredicted words and influence even early first fixation duration behavior. The results suggest that lexical processing can take place within a latent, but nevertheless discrete, space in cases of uncertainty.

Towards a Bayesian hierarchical model of lexical processing

LLM-surprisal is a computational measure of how unexpected a word or character is given the preceding context, as estimated by large language models (LLMs). This study investigated the effectiveness of LLM-surprisal in modeling second language (L2) writing development, focusing on Chinese L2 writing as a case to test its cross-linguistical generalizability. We selected three types of LLMs with different pretraining settings: a multilingual model trained on various languages, a Chinese-general model trained on both Simplified and Traditional Chinese, and a Traditional-Chinese-specific model. This comparison allowed us to explore how model architecture and training data affect LLM-surprisal estimates of learners’ essays written in Traditional Chinese, which in turn influence the modeling of L2 proficiency and development. We also correlated LLM-surprisals with 16 classic linguistic complexity indices (e.g., character sophistication, lexical diversity, syntactic complexity, and discourse coherence) to evaluate its interpretability and validity as a measure of L2 writing assessment. Our findings demonstrate the potential of LLM-surprisal as a robust, interpretable, cross-linguistically applicable metric for automatic writing assessment and contribute to bridging computational and linguistic approaches in understanding and modeling L2 writing development. All analysis scripts are available at https://github.com/JingyingHu/ChineseL2Writing-Surprisals.

Modeling Chinese L2 Writing Development: The LLM-Surprisal Perspective

Animacy is a well-documented factor affecting language production, but its influence on Language Models (LMs) in complex structures like Object Relative Clauses (ORCs) remains underexplored. This study examines LMs' sensitivity to animacy in English ORC structure choice (passive vs. active) using surprisal-based and prompting-based analyses, alongside human baselines. In surprisal-based analysis, DistilGPT-2 best mirrored human preferences, while GPT-Neo and BERT-base showed rigid biases, diverging from human patterns. Prompting-based analysis expanded testing to GPT-4o-mini, Gemini models, and DeepSeek-R1, revealing GPT-4o-mini’s stronger human alignment but limited animacy sensitivity in Gemini models and DeepSeek-R1. Some LMs exhibited inconsistencies between analyses, reinforcing that prompting alone is unreliable for assessing linguistic competence. Corpus analysis confirmed that training data alone cannot fully explain animacy sensitivity, suggesting emergent animacy-aware representations. These findings underscore the interaction between training data, model architecture, and linguistic generalization, highlighting the need for integrating structured linguistic knowledge into LMs to enhance their alignment with human sentence processing mechanisms.

Beyond Binary Animacy: A Multi-Method Investigation of LMs’ Sensitivity in English Object Relative Clauses

Language syllabification is the separation of a word into written or spoken syllables. The study of syllabification plays a pivotal role in morphology and there have been previous attempts to study this phenomenon using graphs or networks. Previous approaches have claimed through visual estimation that the degree distribution of language networks follows the Power Law distribution, however, there have not been any empirically grounded metrics to determine the same. In our study, we implement two kinds of language networks, namely, syllabary and lexical networks, and investigate the syllabification of four European languages: English, French, German and Spanish using network analysis and examine their small-world, random and scale-free nature. We additionally empirically prove that contrary to claims in previous works, although the degree distribution of these networks appear to follow a power law distribution, they are actually more in agreement with a log-normal distribution, when a numerically grounded curve-fitting is applied. Finally, we explore how syllabary and lexical networks for the English language change over time using a database of age-of-acquisition rating words. Our analysis further shows that the preferential attachment mechanism appears to be a well-grounded explanation for the degree distribution of the syllabary network.

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